The Project

The goals / steps of this project are the following:

• Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
• Apply a distortion correction to raw images.
• Use color transforms, gradients, etc., to create a thresholded binary image.
• Apply a perspective transform to rectify binary image ("birds-eye view").
• Detect lane pixels and fit to find the lane boundary.
• Determine the curvature of the lane and vehicle position with respect to center.
• Warp the detected lane boundaries back onto the original image.
• Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

import numpy as np
import cv2
import glob
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import pickle
%matplotlib inline
%matplotlib qt

#找棋盘角，相机标定得到相机矩阵和失真系数

objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)

objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.

# Make a list of calibration images
Chessboard_images = glob.glob('camera_cal/cal*.jpg')

# Step through the list and search for chessboard corners
for idx, fname in enumerate(Chessboard_images):
    chessboard_img = cv2.imread(fname)
    chessboard_gray = cv2.cvtColor(chessboard_img, cv2.COLOR_BGR2GRAY)
    img_size = (chessboard_img.shape[1], chessboard_img.shape[0])
    
    # Find the chessboard corners
    chessboard_ret, chessboard_corners = cv2.findChessboardCorners(chessboard_gray, (9,6), None)

    # If found, add object points, image points
    if chessboard_ret == True:
        objpoints.append(objp)
        imgpoints.append(chessboard_corners)
        print(fname + " test ok.")
        # Draw and display the corners
        cv2.drawChessboardCorners(chessboard_img, (9,6), chessboard_corners, chessboard_ret)
        #write_name = 'corners_found'+str(idx)+'.jpg'
        #cv2.imwrite(write_name, img)
        cv2.imshow('img', chessboard_img)
        cv2.waitKey(500)
    else:
        print(fname + " 棋盘图像没有正确检测")
cv2.destroyAllWindows()

calibrate_ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size, None, None)    
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "camera_cal/wide_dist_pickle.p", "wb" ) )

camera_cal/calibration10.jpg test ok.
camera_cal/calibration18.jpg test ok.
camera_cal/calibration2.jpg test ok.
camera_cal/calibration14.jpg test ok.
camera_cal/calibration13.jpg test ok.
camera_cal/calibration15.jpg test ok.
camera_cal/calibration11.jpg test ok.
camera_cal/calibration7.jpg test ok.
camera_cal/calibration1.jpg 棋盘图像没有正确检测
camera_cal/calibration5.jpg 棋盘图像没有正确检测
camera_cal/calibration8.jpg test ok.
camera_cal/calibration19.jpg test ok.
camera_cal/calibration20.jpg test ok.
camera_cal/calibration16.jpg test ok.
camera_cal/calibration17.jpg test ok.
camera_cal/calibration6.jpg test ok.
camera_cal/calibration4.jpg 棋盘图像没有正确检测
camera_cal/calibration12.jpg test ok.
camera_cal/calibration9.jpg test ok.
camera_cal/calibration3.jpg test ok.

#矫正图像失真
# Test undistortion on an image

def img_undistort(img,mtx,dist):
    dst = cv2.undistort(img, mtx, dist, None, mtx)
    return dst
''' 
img = cv2.imread('camera_cal/calibration2.jpg')
img_size = (img.shape[1], img.shape[0])

# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
print('mtx = ',mtx)
print('dist = ',dist)
print(img.shape)
dst = cv2.undistort(img, mtx, dist, None, mtx)
print(dst.shape)
cv2.imwrite('output_images/test_undist.jpg',dst)

# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "camera_cal/wide_dist_pickle.p", "wb" ) )
#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=30)
'''  

' \nimg = cv2.imread(\'camera_cal/calibration2.jpg\')\nimg_size = (img.shape[1], img.shape[0])\n\n# Do camera calibration given object points and image points\nret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)\nprint(\'mtx = \',mtx)\nprint(\'dist = \',dist)\nprint(img.shape)\ndst = cv2.undistort(img, mtx, dist, None, mtx)\nprint(dst.shape)\ncv2.imwrite(\'output_images/test_undist.jpg\',dst)\n\n# Save the camera calibration result for later use (we won\'t worry about rvecs / tvecs)\ndist_pickle = {}\ndist_pickle["mtx"] = mtx\ndist_pickle["dist"] = dist\npickle.dump( dist_pickle, open( "camera_cal/wide_dist_pickle.p", "wb" ) )\n#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)\n# Visualize undistortion\nf, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))\nax1.imshow(img)\nax1.set_title(\'Original Image\', fontsize=30)\nax2.imshow(dst)\nax2.set_title(\'Undistorted Image\', fontsize=30)\n'

#测试-失真矫正
#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
# Visualize undistortion
img = cv2.imread('camera_cal/calibration2.jpg')
dst_img = img_undistort(img,mtx,dist)
cv2.imwrite('output_images/test_undist.jpg',dst_img)

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst_img)
ax2.set_title('Undistorted Image', fontsize=30)
plt.savefig('./writeup_images/image1.jpg')

#测试-失真矫正
#img = cv2.imread('test_images/test1.jpg')
img_test= mpimg.imread('test_images/test2.jpg')
dst_test = img_undistort(img_test,mtx,dist)
'''
img_size = (img.shape[1], img.shape[0])

# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
print(img.shape)
dst = cv2.undistort(img, mtx, dist, None, mtx)
print(dst.shape)
'''

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img_test)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst_test)
ax2.set_title('Undistorted Image', fontsize=30)
plt.savefig('./writeup_images/image2.jpg')

#将视频中的图像数据提取，进行畸变修正处理后，存储在名为original_image的文件夹中，以供挑选。
video_input = 'project_video.mp4'
cap = cv2.VideoCapture(video_input)
count = 1
while(True):
    ret, image = cap.read()
    if ret:
        undistort_image = img_undistort(image, mtx, dist)
        cv2.imwrite('original_image/' + str(count) + '.jpg', undistort_image)
        count += 1
    else:
        break
cap.release()

#透视变换，图像变成俯视视角
'''

# Read in the saved camera matrix and distortion coefficients
# These are the arrays you calculated using cv2.calibrateCamera()
img1 = cv2.imread('camera_cal/calibration2.jpg')
#dist_pickle = pickle.load( open( "camera_cal/wide_dist_pickle.p", "rb" ) )
#mtx = dist_pickle["mtx"]
#dist = dist_pickle["dist"]
# Read in an image
nx = 9 # the number of inside corners in x
ny = 6 # the number of inside corners in y
# MODIFY THIS FUNCTION TO GENERATE OUTPUT 
# THAT LOOKS LIKE THE IMAGE ABOVE
def corners_unwarp(img, nx, ny, mtx_un, dist_un):
    undist_un = cv2.undistort(img, mtx_un, dist_un, None, mtx_un)
    gray_un = cv2.cvtColor(undist_un, cv2.COLOR_BGR2GRAY)
    ret_un, corners_un = cv2.findChessboardCorners(gray_un, (nx,ny), None)
    print(ret_un)
    if ret_un == True:
        # Draw and display the corners
        cv2.drawChessboardCorners(undist_un, (nx,ny), corners_un, ret_un)
        img_size_un = (undist_un.shape[1],undist_un.shape[0])
        src_un = np.float32([corners_un[0],corners_un[nx-1],corners_un[-1],corners_un[-nx]])
        offset_un = 100
        dst_un = np.float32([[offset_un,offset_un],[img_size_un[0]-offset_un,offset_un],[img_size_un[0]-offset_un,img_size_un[1]-offset_un],[offset_un,img_size_un[1]-offset_un]])
        M_un = cv2.getPerspectiveTransform(src_un, dst_un)
        warped_un = cv2.warpPerspective(undist_un, M_un, img_size_un, flags=cv2.INTER_LINEAR)
    return warped_un
'''
#perspective_M= corners_unwarp(img1, nx, ny, mtx, dist)
'''
img1 = mpimg.imread('test_images/straight_lines1.jpg')
img_size = (img1.shape[1], img1.shape[0])
'''
def warper(img):
    img_size = (img.shape[1], img.shape[0])
    '''
    src = np.float32(
    [[(img_size[0] / 2) - 55, img_size[1] / 2 + 100],
    [((img_size[0] / 6) - 10), img_size[1]],
    [(img_size[0] * 5 / 6) + 60, img_size[1]],
    [(img_size[0] / 2 + 55), img_size[1] / 2 + 100]])
    dst = np.float32(
    [[(img_size[0] / 4), 0],
    [(img_size[0] / 4), img_size[1]],
    [(img_size[0] * 3 / 4), img_size[1]],
    [(img_size[0] * 3 / 4), 0]])
    
    src = np.float32([[580, 460], [700, 460], [1096, 720], [200, 720]])
    dst = np.float32([[300, 0], [950, 0], [956, 720], [296, 720]])
    '''
    src = np.float32([[580, 460], [700, 460], [1096, 720], [200, 720]])
    dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
    # Compute and apply perpective transform
    M = cv2.getPerspectiveTransform(src, dst)
    Minv = cv2.getPerspectiveTransform(dst, src)
    #print(M)
    #print(Minv)
    warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_NEAREST)  # keep same size as input image
    return warped,Minv
'''
distort_image = undistortImage(test_distort_image, mtx, dist)
top_down_warped = warper(img1,src,dst)

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img1)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(top_down_warped)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
'''

"\ndistort_image = undistortImage(test_distort_image, mtx, dist)\ntop_down_warped = warper(img1,src,dst)\n\nf, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))\nf.tight_layout()\nax1.imshow(img1)\nax1.set_title('Original Image', fontsize=50)\nax2.imshow(top_down_warped)\nax2.set_title('Undistorted and Warped Image', fontsize=50)\nplt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)\n"

#测试-透视变换（对原始直道图像）
test_img1 = mpimg.imread('test_images/straight_lines1.jpg')
test_distort_image1 = img_undistort(test_img1,mtx,dist)
test_top_down_warped1,test_Minv1 = warper(test_distort_image1)
'''
img_size = (img1.shape[1], img1.shape[0])
'''
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(test_distort_image1)
ax1.set_title('Undistorted Image', fontsize=50)
ax2.imshow(test_top_down_warped1)
ax2.set_title('Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.savefig('./writeup_images/image3.jpg')

#测试-透视变换(对已经失真矫正图像)
top_down_warped_test,Minv_test = warper(dst_test)
#cv2.imwrite('output_images/top_down_warped.jpg',top_down_warped4)

#img_size = (img1.shape[1], img1.shape[0])

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(dst_test)
ax1.set_title('Undistorted Image', fontsize=50)
ax2.imshow(top_down_warped_test)
ax2.set_title('Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.savefig('./writeup_images/image4.jpg')

#测试1-透视变换（对原始图像）
img1 = mpimg.imread('test_images/test4.jpg')
distort_image1 = img_undistort(img1,mtx,dist)
top_down_warped1,Minv1 = warper(distort_image1)
'''
img_size = (img1.shape[1], img1.shape[0])
'''
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img1)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(top_down_warped1)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

#测试2-透视变换（对原始图像）
img2 = mpimg.imread('test_images/test6.jpg')
distort_image2 = img_undistort(img2,mtx,dist)
top_down_warped2,Minv2 = warper(distort_image2)
'''
img_size = (img1.shape[1], img1.shape[0])
'''
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img2)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(top_down_warped2)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

#测试3-透视变换（对已经失真矫正图像）
dst_img3 = mpimg.imread('original_image/575.jpg')
top_down_warped3,Minv3 = warper(dst_img3 )
#cv2.imwrite('output_images/top_down_warped.jpg',top_down_warped4)

#img_size = (img1.shape[1], img1.shape[0])

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(dst_img3)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(top_down_warped3)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

#测试4-透视变换（对已经失真矫正，并通过色彩和渐变空间输出的二值化图像）
dst_img4 = mpimg.imread('original_image/1038.jpg')
top_down_warped4,Minv4 = warper(dst_img4)
#cv2.imwrite('output_images/top_down_warped.jpg',top_down_warped2)

#img_size = (img1.shape[1], img1.shape[0])

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(dst_img4)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(top_down_warped4)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

#测试5-透视变换#（对已经失真矫正，并通过色彩和渐变空间输出的二值化图像）
dst_img5 = mpimg.imread('original_image/1047.jpg')
top_down_warped5,Minv5 = warper(dst_img5)
#cv2.imwrite('output_images/top_down_warped.jpg',top_down_warped2)

#img_size = (img1.shape[1], img1.shape[0])

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(dst_img5)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(top_down_warped5)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

'''
curvature=1000.2324
direction = 'Left'
offset = 0.124
info_cur = "Curvature {:6.1f} m".format(curvature)
info_offset = "Off center: {0} {1:3.3f}m".format(direction, offset)
cv2.putText(dst_img5,info_cur,(400,50),cv2.FONT_HERSHEY_PLAIN,2.0,(0,0,255),2)
cv2.putText(dst_img5,info_offset,(400,150),cv2.FONT_HERSHEY_PLAIN,2.0,(0,0,255),2)
plt.imshow(dst_img5)
'''

'\ncurvature=1000.2324\ndirection = \'Left\'\noffset = 0.124\ninfo_cur = "Curvature {:6.1f} m".format(curvature)\ninfo_offset = "Off center: {0} {1:3.3f}m".format(direction, offset)\ncv2.putText(dst_img5,info_cur,(400,50),cv2.FONT_HERSHEY_PLAIN,2.0,(0,0,255),2)\ncv2.putText(dst_img5,info_offset,(400,150),cv2.FONT_HERSHEY_PLAIN,2.0,(0,0,255),2)\nplt.imshow(dst_img5)\n'

#测试6-透视变换#（对已经失真矫正，并通过色彩和渐变空间输出的二值化图像）
dst_img6 = mpimg.imread('original_image/560.jpg')
top_down_warped6,Minv6 = warper(dst_img6)
#cv2.imwrite('output_images/top_down_warped.jpg',top_down_warped2)

#img_size = (img1.shape[1], img1.shape[0])

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(dst_img6)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(top_down_warped6)
ax2.set_title('Undistorted and Warped Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

#渐变和色彩空间,输出二值化图像

def abs_sobel_thresh(img,orient,sxy_thresh):
     
    gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    if orient == 'x':
        sobel = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0, ksize=3) # Take the derivative in x
    if orient == 'y':
        sobel = cv2.Sobel(gray_img, cv2.CV_64F, 0, 1, ksize=3) # Take the derivative in y
    abs_sobel = np.absolute(sobel)
    scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
    sxybinary = np.zeros_like(scaled_sobel)
    sxybinary[(scaled_sobel >= sxy_thresh[0]) & (scaled_sobel <= sxy_thresh[1])] = 1
    binary_output = sxybinary
    return binary_output

def mag_thresh(img, sobel_kernel, mag_thresh):
    gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    sobelx = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray_img, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
    magnitude = np.sqrt(sobelx**2 + sobely**2)
    scaled_sobel = np.uint8(255*magnitude/np.max(magnitude))
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= mag_thresh[0]) & (scaled_sobel <= mag_thresh[1])] = 1
    binary_output = sxbinary
    return binary_output

def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
    gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    sobelx = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray_img, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
    absgraddir = np.arctan2(np.absolute(sobely),np.absolute(sobelx))
    binary_output =  np.zeros_like(absgraddir)
    binary_output[(absgraddir>= thresh[0]) & (absgraddir <= thresh[1])] = 1
    return binary_output

# Edit this function to create your own pipeline.
def pipeline(img, r_thresh=(230, 255), l_thresh=(210,255),s_thresh=(120,255),b_lab_thresh=(220,255),sxy_thresh=(45, 100)):#170, 255

    #RGB
    r_rgb = img[:,:,0]
    r_binary = np.zeros_like(r_rgb)
    r_binary[(r_rgb >= r_thresh[0]) & (r_rgb <= r_thresh[1])] = 1
    
    # HLS color space
    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
    l_channel = hls[:,:,1]
    l_binary = np.zeros_like(l_channel)
    l_binary[(l_channel >= l_thresh[0]) & (l_channel <= l_thresh[1])] = 1
    
    s_channel = hls[:,:,2]
    s_binary = np.zeros_like(s_channel)
    s_binary[(s_channel >= s_thresh[0]) & (s_channel <= s_thresh[1])] = 1
    
    #Lab
    lab = cv2.cvtColor(img, cv2.COLOR_RGB2Lab)
    b_lab = lab[:,:,2]
    if np.max(b_lab) > 100:
        b_lab = b_lab*(255/np.max(b_lab))
    b_binary = np.zeros_like(b_lab)
    b_binary[((b_lab > b_lab_thresh[0]) & (b_lab <= b_lab_thresh[1]))] = 1
    
    '''
    # Sobel x
    gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    sobelx = cv2.Sobel(gray_img, cv2.CV_64F, 1, 0, ksize=3) # Take the derivative in x
    abs_sobelx = np.absolute(sobelx) # Absolute x derivative to accentuate lines away from horizontal
    scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))   
    #Sobel
    
    # Threshold x gradient
    sxbinary = np.zeros_like(scaled_sobel)
    sxbinary[(scaled_sobel >= sx_thresh[0]) & (scaled_sobel <= sx_thresh[1])] = 1
    '''
    
    sxbinary=abs_sobel_thresh(img,'x',sxy_thresh)
    dir_binary = dir_threshold(img, sobel_kernel=3, thresh=(0.7, 1.3))
    
    # Stack each channel
    #color_binary = np.dstack(( np.zeros_like(sxbinary), sxbinary, s_binary)) * 255
    color_binary = np.dstack((r_binary, sxbinary, l_binary)) * 255
   # Combine the two binary thresholds
    combined_binary = np.zeros_like(sxbinary)
    combined_binary[(s_binary==1)&(dir_binary==1)|(r_binary == 1)|(l_binary == 1)|(sxbinary == 1)] = 1#(b_binary == 1)
    return color_binary,combined_binary

#测试-渐变和色彩空间,输出二值化图像
color_result_test,combined_binary_test = pipeline(top_down_warped_test)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()

ax1.imshow(top_down_warped_test)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(combined_binary_test,cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)
#plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
plt.savefig('./writeup_images/image5.jpg')

#测试1-渐变和色彩空间,输出二值化图像
color_result1,combined_binary1 = pipeline(top_down_warped1)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()

ax1.imshow(top_down_warped1)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(combined_binary1,cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)
#plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)

Text(0.5, 1.0, 'Pipeline Result')

#测试2-渐变和色彩空间,输出二值化图像
color_result2,combined_binary2= pipeline(top_down_warped2)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()

ax1.imshow(top_down_warped2)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(combined_binary2,cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)

Text(0.5, 1.0, 'Pipeline Result')

#测试3-渐变和色彩空间,输出二值化图像
color_result3,combined_binary3 = pipeline(top_down_warped3)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()

ax1.imshow(top_down_warped3)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(combined_binary3,cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)

Text(0.5, 1.0, 'Pipeline Result')

#测试4-渐变和色彩空间,输出二值化图像
color_result4,combined_binary4 = pipeline(top_down_warped4)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()

ax1.imshow(top_down_warped4)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(combined_binary4,cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)

Text(0.5, 1.0, 'Pipeline Result')

#测试5-渐变和色彩空间,输出二值化图像
color_result5,combined_binary5 = pipeline(top_down_warped5)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()

ax1.imshow(top_down_warped5)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(combined_binary5,cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)

Text(0.5, 1.0, 'Pipeline Result')

#测试6-渐变和色彩空间,输出二值化图像
color_result6,combined_binary6 = pipeline(top_down_warped6)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()

ax1.imshow(top_down_warped6)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(combined_binary6,cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)

Text(0.5, 1.0, 'Pipeline Result')

#单帧图像中车道线检测

def find_lane_pixels(binary_warped):
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
    # Create an output image to draw on and visualize the result
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255

    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]//2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint
    #plt.imshow(out_img)
    #plt.imshow(binary_warped,cmap='gray')
    # HYPERPARAMETERS
    # Choose the number of sliding windows
    nwindows = 9
    # Set the width of the windows +/- margin
    margin = 60
    # Set minimum number of pixels found to recenter window
    minpix = 40

    # Set height of windows - based on nwindows above and image shape
    window_height = np.int(binary_warped.shape[0]//nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    #print(nonzerox.shape)
    # Current positions to be updated later for each window in nwindows
    leftx_current = leftx_base
    rightx_current = rightx_base

    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        ### TO-DO: Find the four below boundaries of the window ###
        win_xleft_low = leftx_current - margin  # Update this
        win_xleft_high = leftx_current + margin # Update this
        win_xright_low = rightx_current - margin  # Update this
        win_xright_high = rightx_current + margin  # Update this
        #print('(',win_xleft_low,',',win_y_low,')',' (',win_xleft_high,',',win_y_high,')')
        # Draw the windows on the visualization image
        cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2) 
        cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2) 
        
        ### TO-DO: Identify the nonzero pixels in x and y within the window ###
        good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & 
        (nonzerox >= win_xleft_low) &  (nonzerox < win_xleft_high)).nonzero()[0]
        good_right_inds = ((nonzeroy >= win_y_low) &(nonzeroy < win_y_high)
        & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
        
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        
        ### TO-DO: If you found > minpix pixels, recenter next window ###
        ### (`right` or `leftx_current`) on their mean position ###
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:        
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

    # Concatenate the arrays of indices (previously was a list of lists of pixels)
    try:
        left_lane_inds = np.concatenate(left_lane_inds)
        right_lane_inds = np.concatenate(right_lane_inds)
    except ValueError:
        # Avoids an error if the above is not implemented fully
        pass

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds] 
    if len(leftx) == 0:
        print('leftx:0')
    if len(lefty) == 0:
        print('lefty:0')
    return leftx, lefty, rightx, righty, out_img

def fit_polynomial(binary_warped):
    # Find our lane pixels first
    leftx, lefty, rightx, righty, out_img = find_lane_pixels(binary_warped)

    ### TO-DO: Fit a second order polynomial to each using `np.polyfit` ###
    
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    print(left_fit)
    print(right_fit)
    # Generate x and y values for plotting
    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])
    try:
        left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
        right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    except TypeError:
        # Avoids an error if `left` and `right_fit` are still none or incorrect
        print('The function failed to fit a line!')
        left_fitx = 1*ploty**2 + 1*ploty
        right_fitx = 1*ploty**2 + 1*ploty
    ## Visualization ##
    # Colors in the left and right lane regions
    out_img[lefty, leftx] = [255, 0, 0]
    out_img[righty, rightx] = [0, 0, 255]

    # Plots the left and right polynomials on the lane lines
    plt.plot(left_fitx, ploty, color='yellow')
    plt.plot(right_fitx, ploty, color='yellow')

    return left_fit,right_fit,leftx,lefty,rightx,righty,left_fitx, right_fitx, ploty,out_img

#测试-单帧图像中车道线检测
# Load our image
binary_warped_test = combined_binary_test

left_fit,right_fit,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx, ploty, result = fit_polynomial(binary_warped_test)
print(left_fit)
print(right_fit)
plt.imshow(result)
plt.savefig('./writeup_images/image6.jpg')

[-2.26772384e-04  3.19644066e-01  2.52819093e+02]
[-1.70377536e-04  2.81282616e-01  9.07650921e+02]
[-2.26772384e-04  3.19644066e-01  2.52819093e+02]
[-1.70377536e-04  2.81282616e-01  9.07650921e+02]

#两帧图像中车道线检测

def fit_poly(img_shape, leftx, lefty, rightx, righty):
    ### TO-DO: Fit a second order polynomial to each with np.polyfit() ###
    left_fit = np.polyfit(lefty,leftx,2)
    right_fit = np.polyfit(righty,rightx,2)
    # Generate x and y values for plotting
    ploty = np.linspace(0, img_shape[0]-1, img_shape[0])
    ### TO-DO: Calc both polynomials using ploty, left_fit and right_fit ###
    left_fitx = left_fit[0]*(ploty**2) + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*(ploty**2) + right_fit[1]*ploty + right_fit[2]
    return left_fit, right_fit, left_fitx, right_fitx, ploty

def search_around_poly(left_fit,right_fit,binary_warped):
    # HYPERPARAMETER
    # Choose the width of the margin around the previous polynomial to search
    # The quiz grader expects 100 here, but feel free to tune on your own!
    margin = 60

    # Grab activated pixels
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    
    ### TO-DO: Set the area of search based on activated x-values ###
    ### within the +/- margin of our polynomial function ###
    ### Hint: consider the window areas for the similarly named variables ###
    ### in the previous quiz, but change the windows to our new search area ###
    left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin))
        & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
    right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin))
        & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
    
    # Again, extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds]
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds]

    # Fit new polynomials
    left_fit, right_fit, left_fitx, right_fitx, ploty = fit_poly(binary_warped.shape, leftx, lefty, rightx, righty)
    print(left_fit)
    print(right_fit)
    ## Visualization ##
    # Create an image to draw on and an image to show the selection window
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
    window_img = np.zeros_like(out_img)
    # Color in left and right line pixels
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]

    # Generate a polygon to illustrate the search window area
    # And recast the x and y points into usable format for cv2.fillPoly()
    left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
    left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
    left_line_pts = np.hstack((left_line_window1, left_line_window2))
    right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
    right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
    right_line_pts = np.hstack((right_line_window1, right_line_window2))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
    cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
    result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
    
    # Plot the polynomial lines onto the image
    plt.plot(left_fitx, ploty, color='yellow')
    plt.plot(right_fitx, ploty, color='yellow')
    ## End visualization steps ##
    
    return left_fit,right_fit,leftx,lefty,rightx,righty,left_fitx, right_fitx, ploty, result

#测试-两帧图像中车道线检测

#Load our image - this should be a new frame since last time!
binary_warped_img = combined_binary_test

# Polynomial fit values from the previous frame
# Make sure to grab the actual values from the previous step in your project!
print(left_fit)
print(right_fit)

# Run image through the pipeline
# Note that in your project, you'll also want to feed in the previous fits
left_fit, right_fit, warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx, ploty,result = search_around_poly(left_fit, right_fit, binary_warped_img)
print(left_fit)
print(right_fit)
# View your output
plt.imshow(result)
plt.savefig('./writeup_images/image7.jpg')

[-2.26772384e-04  3.19644066e-01  2.52819093e+02]
[-1.70377536e-04  2.81282616e-01  9.07650921e+02]
[-2.26772384e-04  3.19644066e-01  2.52819093e+02]
[-1.70990391e-04  2.82888937e-01  9.07046940e+02]
[-2.26772384e-04  3.19644066e-01  2.52819093e+02]
[-1.70990391e-04  2.82888937e-01  9.07046940e+02]

#车道线曲率和当前测偏距离计算
#x_in_meter= mx / (my**2) *a(y_in_meter**2)+(mx/my)b*y_in_meter+mx*c
#x=ay**2+by+c, mx=x_in_meter/mx, my=y_in_meter/my
   
def measure_curvature_real():
    '''
    Calculates the curvature of polynomial functions in meters.
    '''
    # Define conversions in x and y from pixels space to meters
    ym_per_pix = 30/720 # meters per pixel in y dimension
    xm_per_pix = 3.7/700 # meters per pixel in x dimension
    
    # Start by generating our fake example data
    # Make sure to feed in your real data instead in your project!
    #left_fit_cr = np.polyfit(warped_lefty*ym_per_pix, warped_leftx*xm_per_pix, 2)
    #right_fit_cr = np.polyfit(warped_righty*ym_per_pix, warped_rightx*xm_per_pix, 2)
    left_fit_cr = np.polyfit(ploty*ym_per_pix, left_fitx*xm_per_pix, 2)
    right_fit_cr = np.polyfit(ploty*ym_per_pix, right_fitx*xm_per_pix, 2)
    # Define y-value where we want radius of curvature
    # We'll choose the maximum y-value, corresponding to the bottom of the image
    #ploty = np.linspace(0, binary_warped_img.shape[0]-1, binary_warped_img.shape[0])
    y_eval = np.max(ploty)
    
    ##### TO-DO: Implement the calculation of R_curve (radius of curvature) #####
    left_curverad = (1+(2*left_fit_cr[0]*y_eval*ym_per_pix+left_fit_cr[1])**2)**1.5/np.absolute(2*left_fit_cr[0])  ## Implement the calculation of the left line here
    right_curverad = (1+(2*right_fit_cr[0]*y_eval*ym_per_pix+right_fit_cr[1])**2)**1.5/np.absolute(2*right_fit_cr[0])  ## Implement the calculation of the right line here
    curve_direction = (left_curverad+right_curverad)/2.0
    
    #left_down_realx = left_fit_cr[0]*((y_eval*ym_per_pix)**2)+left_fit_cr[1]*(y_eval*ym_per_pix)+left_fit_cr[2]
    #right_down_realx =right_fit_cr[0]*((y_eval*ym_per_pix)**2)+right_fit_cr[1]*(y_eval*ym_per_pix)+right_fit_cr[2]
    #distance_from_center = (right_down_realx - left_down_realx)/2 - (binary_warped_img.shape[1]/2)*xm_per_pix
    lane_width = np.absolute(left_fitx[-1] - right_fitx[-1])
    lane_xm_per_pix=3.7/lane_width
    veh_pos = (((left_fitx[719] + right_fitx[719]) * lane_xm_per_pix) / 2.)
    cen_pos = ((binary_warped_img.shape[1] * lane_xm_per_pix) / 2.)
    distance_from_center = veh_pos - cen_pos
    
    return left_curverad, right_curverad, curve_direction, distance_from_center

#测试-车道线曲率和当前测偏距离计算
# Calculate the radius of curvature in meters for both lane lines
left_curverad, right_curverad , curve_direction, distance_real = measure_curvature_real()

print(left_curverad, 'm', right_curverad, 'm')
print("车道曲率为：",curve_direction,'m')
print("车辆相对于车道中心的真实位置：",distance_real,'m')
# Should see values of 533.75 and 648.16 here, if using
# the default `generate_data` function with given seed number

724.1926421538909 m 960.4759318338192 m
车道曲率为： 842.3342869938551 m
车辆相对于车道中心的真实位置： 0.3027542123037241 m

# 将检测到的车道边界扭曲回原始图像
def drawing(undist, bin_warped,Minv, left_fitx, right_fitx, ploty):
    # Create an image to draw the lines on
    warp_zero = np.zeros_like(bin_warped).astype(np.uint8)
    color_warp = np.dstack((warp_zero, warp_zero, warp_zero))

    # Recast the x and y points into usable format for cv2.fillPoly()
    pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
    pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
    pts = np.hstack((pts_left, pts_right))

    # Draw the lane onto the warped blank image
    cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))

    # Warp the blank back to original image space using inverse perspective matrix (Minv)
    newwarp = cv2.warpPerspective(color_warp, Minv, (undist.shape[1], undist.shape[0])) 
    # Combine the result with the original image
    line_img = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
    
    return line_img

#测试-将检测到的车道边界扭曲回原始图像
draw_result = drawing(dst_test , combined_binary_test,Minv_test, left_fitx, right_fitx, ploty)
plt.imshow(draw_result)
plt.savefig('./writeup_images/image8.jpg')

#视频上显示实时文字(车道线曲率和当前测偏距离)
def putimg(line_img, curvature, offset):
    info_cur="Curvature: {:6.3f} m".format(curvature)
    #info_offset = "Off center: {0} {1:3.3f}m".format(direction, offset)
    info_offset = "Off center: {:3.3f}m".format(offset)
    cv2.putText(line_img,info_cur,(100,50),cv2.FONT_HERSHEY_PLAIN,2.0,(255,255,0),2)
    cv2.putText(line_img,info_offset,(100,150),cv2.FONT_HERSHEY_PLAIN,2.0,(255,255,0),2)
    result = line_img
    return result

output_result=putimg(draw_result, curve_direction, distance_real)
plt.imshow(output_result)
plt.savefig('./writeup_images/image9.jpg')

'''
undistort_image = img_undistort(image, mtx, dist)
top_down_warped,Minv = warper(undistort_image)
color_result_img,combined_binary_img = pipeline(top_down_warped)
left_fit,right_fit,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = fit_polynomial(combined_binary_img)
draw_result = drawing(undistort_image , combined_binary_img,Minv, left_fitx, right_fitx, ploty)
'''

'\nundistort_image = img_undistort(image, mtx, dist)\ntop_down_warped,Minv = warper(undistort_image)\ncolor_result_img,combined_binary_img = pipeline(top_down_warped)\nleft_fit,right_fit,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = fit_polynomial(combined_binary_img)\ndraw_result = drawing(undistort_image , combined_binary_img,Minv, left_fitx, right_fitx, ploty)\n'

'''
nx = 9 
ny = 6
ret, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)

src = np.float32([[580, 460], [700, 460], [1096, 720], [200, 720]])
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
'''

dist_pickle = pickle.load( open( "camera_cal/wide_dist_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]

video_input = 'project_video.mp4'
video_output = 'project_result_video.mp4'

cap = cv2.VideoCapture(video_input)

fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(video_output, fourcc, 20.0, (1280, 720))

detected = False
left_fit = []
right_fit = []
ploty = []
i=0
while(True):
    ret, image = cap.read()
    #print(ret)
    if ret:
        i=i+1
        print(i)
        undistort_image = img_undistort(image, mtx, dist)
        #color_result_img,combined_binary_img = pipeline(undistort_image)
        #top_down_warped_img,Minv = warper(combined_binary_img)
        top_down_warped,Minv = warper(undistort_image)
        color_result_img,combined_binary_img = pipeline(top_down_warped)
        '''
        warp_image, M, Minv = warpImage(undistort_image, src, dst)
        hlsL_binary = hlsLSelect(warp_image)
        labB_binary = labBSelect(warp_image, (205, 255))
        combined_binary = np.zeros_like(sx_binary)
        combined_binary[(hlsL_binary == 1) | (labB_binary == 0)] = 1
        '''
        #print('detected = ',detected)
        #left_fit_warped,right_fit_warped,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = fit_polynomial(combined_binary_img)
        
        if detected == False:
            left_fit_warped,right_fit_warped,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = fit_polynomial(combined_binary_img)
            #out_img, left_fit, right_fit, ploty = fit_polynomial(combined_binary, nwindows=9, margin=80, minpix=40)
            #print(len(right_fit))
            #print(len(right_fit)>0 and len(left_fit)>0)
            print('k')
            if (len(left_fit_warped) > 0 and len(right_fit_warped) > 0) :
                detected = True
            else :
                #print('1')
                detected = False
        else:            
            left_fit_warped,right_fit_warped,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = search_around_poly(left_fit_warped,right_fit_warped,combined_binary_img)
            #track_result, left_fit, right_fit, ploty,  = search_around_poly(combined_binary, left_fit, right_fit)
            print('l')
            #print(left_fit_warped)
            #print(right_fit_warped)
            if (len(left_fit_warped) > 0 and len(right_fit_warped) > 0) :
                detected = True
            else :
                detected = False
        
        left_curverad, right_curverad , curve_direction, distance_real = measure_curvature_real()
        #result = drawing(undistort_image, combined_binary, warp_image, left_fitx, right_fitx)
        draw_result = drawing(undistort_image , combined_binary_img,Minv, left_fitx, right_fitx, ploty)
        output_result = putimg(draw_result, curve_direction, distance_real)
        out.write(output_result)
    else:
        break
        
cap.release()
out.release()

1
[-2.28047868e-04  3.09390436e-01  2.54971313e+02]
[-1.83976516e-04  2.84815326e-01  9.14452760e+02]
k
2
[-2.36121500e-04  3.18984568e-01  2.53531290e+02]
[-1.49111260e-04  2.65374842e-01  9.12249326e+02]
l
3
[-2.49656812e-04  3.33074921e-01  2.51039476e+02]
[-2.32513192e-04  3.46853764e-01  8.92412856e+02]
l
4
[-2.54730724e-04  3.40244275e-01  2.49480073e+02]
[-2.41672418e-04  3.67972266e-01  8.82930677e+02]
l
5
[-2.73120841e-04  3.57741202e-01  2.45954617e+02]
[-2.30293146e-04  3.59973113e-01  8.83592589e+02]
l
6
[-2.72179440e-04  3.62091538e-01  2.44396821e+02]
[-2.05381065e-04  3.46355282e-01  8.81029443e+02]
l
7
[-2.49972063e-04  3.51233461e-01  2.43341703e+02]
[-1.93068292e-04  3.32648272e-01  8.85511872e+02]
l
8
[-2.49245798e-04  3.55561411e-01  2.42010446e+02]
[-9.92411264e-05  2.94900717e-01  8.80824685e+02]
l
9
[-2.44202909e-04  3.55379405e-01  2.39376717e+02]
[-4.01747193e-05  2.47741961e-01  8.90681183e+02]
l
10
[-2.34611716e-04  3.53265693e-01  2.36415004e+02]
[-5.7429826e-05  2.5750167e-01  8.8857510e+02]
l
11
[-2.27496749e-04  3.51096618e-01  2.35196918e+02]
[-7.50385929e-05  2.70796516e-01  8.83009752e+02]
l
12
[-2.10747337e-04  3.45874199e-01  2.28723658e+02]
[-8.04993372e-05  2.46647836e-01  9.01761106e+02]
l
13
[-2.07567909e-04  3.46230253e-01  2.27655863e+02]
[-5.52199580e-05  2.35776535e-01  8.97543866e+02]
l
14
[-1.88404712e-04  3.32533983e-01  2.28960726e+02]
[-6.32316725e-05  2.39539898e-01  8.95250530e+02]
l
15
[-1.83368268e-04  3.25538807e-01  2.32130921e+02]
[-1.62867257e-04  3.39440766e-01  8.65199259e+02]
l
16
[-1.78836975e-04  3.21020438e-01  2.33868438e+02]
[-1.86042630e-04  3.72621175e-01  8.48294364e+02]
l
17
[-1.74314561e-04  3.14779471e-01  2.34255468e+02]
[-1.65454556e-04  3.56174797e-01  8.48489423e+02]
l
18
[-1.60597303e-04  3.03735315e-01  2.35809078e+02]
[-1.56530216e-04  3.52864285e-01  8.44762662e+02]
l
19
[-1.52004720e-04  3.01296769e-01  2.32239505e+02]
[-1.51355000e-04  3.35902781e-01  8.54589526e+02]
l
20
[-1.40160860e-04  2.92335223e-01  2.31469040e+02]
[-1.48728828e-04  3.23203136e-01  8.61525778e+02]
l
21
[-1.26589062e-04  2.83047794e-01  2.29937088e+02]
[-1.44240673e-04  3.04283680e-01  8.72615972e+02]
l
22
[-1.16752993e-04  2.77740730e-01  2.27272076e+02]
[-1.37091985e-04  2.84709216e-01  8.84210684e+02]
l
23
[-1.20448473e-04  2.78016843e-01  2.25052212e+02]
[-1.42623992e-04  2.77682261e-01  8.91650443e+02]
l
24
[-1.32715327e-04  2.80036012e-01  2.27588875e+02]
[-1.71780621e-04  3.04763585e-01  8.83497849e+02]
l
25
[-1.45270614e-04  2.83979178e-01  2.28961016e+02]
[-1.55998797e-04  2.99073528e-01  8.80656431e+02]
l
26
[-1.50919108e-04  2.87216486e-01  2.26263696e+02]
[-1.49946618e-04  2.78914382e-01  8.93769672e+02]
l
27
[-1.61560355e-04  2.91933550e-01  2.26039725e+02]
[-1.65334559e-04  2.88511902e-01  8.94420480e+02]
l
28
[-1.63399206e-04  2.94503886e-01  2.22645802e+02]
[-1.23644850e-04  2.37871082e-01  9.13067567e+02]
l
29
[-1.78326833e-04  3.00842038e-01  2.24732691e+02]
[-1.54437974e-04  2.71538558e-01  9.02946042e+02]
l
30
[-2.07984359e-04  3.17147804e-01  2.26385478e+02]
[-2.26297335e-04  3.50879624e-01  8.79908583e+02]
l
31
[-2.29991739e-04  3.30217712e-01  2.28475144e+02]
[-2.24255404e-04  3.62986698e-01  8.70990718e+02]
l
32
[-2.48648448e-04  3.40845927e-01  2.31207375e+02]
[-2.20402769e-04  3.76658663e-01  8.58198895e+02]
l
33
[-2.49642285e-04  3.43201320e-01  2.29331650e+02]
[-1.93909997e-04  3.47008691e-01  8.70005164e+02]
l
34
[-2.57213211e-04  3.49348016e-01  2.30151840e+02]
[-1.74776944e-04  3.44193424e-01  8.65425135e+02]
l
35
[-2.67580351e-04  3.58946752e-01  2.29552602e+02]
[-1.62969737e-04  3.37959696e-01  8.65169921e+02]
l
36
[-2.74250045e-04  3.65478545e-01  2.30379478e+02]
[-2.07519847e-04  3.76414703e-01  8.54094903e+02]
l
37
[-2.83454754e-04  3.75830175e-01  2.28023457e+02]
[-1.84330153e-04  3.60573213e-01  8.54216011e+02]
l
38
[-3.06710811e-04  3.96293133e-01  2.25922034e+02]
[-1.53250692e-04  3.42318965e-01  8.53672047e+02]
l
39
[-3.18286664e-04  4.07340664e-01  2.25073520e+02]
[-2.47994599e-04  4.23450987e-01  8.34343285e+02]
l
40
[-3.14382642e-04  4.09563730e-01  2.22258805e+02]
[-2.88521311e-04  4.54342512e-01  8.31262352e+02]
l
41
[-3.19975981e-04  4.20011314e-01  2.18870421e+02]
[-2.73209411e-04  4.46602160e-01  8.30892377e+02]
l
42
[-3.28155654e-04  4.30811533e-01  2.15883134e+02]
[-2.43609968e-04  4.21775777e-01  8.35951487e+02]
l
43
[-3.25339912e-04  4.35220699e-01  2.12305935e+02]
[-2.45516734e-04  4.18928426e-01  8.40403300e+02]
l
44
[-3.10004984e-04  4.27168460e-01  2.10660050e+02]
[-2.36748383e-04  4.01152570e-01  8.49685257e+02]
l
45
[-3.04694650e-04  4.27959586e-01  2.08225118e+02]
[-2.29129507e-04  3.89609206e-01  8.55663792e+02]
l
46
[-2.93923770e-04  4.22649111e-01  2.07399036e+02]
[-1.42471452e-04  3.37981358e-01  8.60865011e+02]
l
47
[-2.81377420e-04  4.16091061e-01  2.05882572e+02]
[-1.76934634e-04  3.49202036e-01  8.63458580e+02]
l
48
[-2.73088752e-04  4.13492014e-01  2.05128339e+02]
[-1.74442018e-04  3.54485043e-01  8.58121141e+02]
l
49
[-2.54980029e-04  4.03882390e-01  2.02766463e+02]
[-1.51605986e-04  3.23162522e-01  8.70581581e+02]
l
50
[-2.38620445e-04  3.94677801e-01  2.02620507e+02]
[-1.90271942e-04  3.59120156e-01  8.61436047e+02]
l
51
[-2.26124607e-04  3.87716623e-01  2.01687313e+02]
[-1.87463134e-04  3.55660624e-01  8.61904317e+02]
l
52
[-2.03483314e-04  3.66721349e-01  2.06906927e+02]
[-1.57922148e-04  3.41150867e-01  8.56930303e+02]
l
53
[-1.86668182e-04  3.53595784e-01  2.08437364e+02]
[-2.57437304e-04  4.27648466e-01  8.38352998e+02]
l
54
[-1.80243255e-04  3.47661497e-01  2.09008951e+02]
[-2.66753307e-04  4.45868449e-01  8.29943844e+02]
l
55
[-1.69298080e-04  3.38039937e-01  2.09164897e+02]
[-2.55659501e-04  4.34036544e-01  8.33826963e+02]
l
56
[-1.53903950e-04  3.24744932e-01  2.10456905e+02]
[-2.36397660e-04  4.13407050e-01  8.39608611e+02]
l
57
[-1.47391702e-04  3.16122548e-01  2.12533399e+02]
[-2.19236077e-04  4.01724132e-01  8.40004310e+02]
l
58
[-1.27289979e-04  2.98633198e-01  2.14510085e+02]
[-1.86847625e-04  3.77504395e-01  8.43726994e+02]
l
59
[-1.38860393e-04  2.98748792e-01  2.19259589e+02]
[-1.65940331e-04  3.83569301e-01  8.30237271e+02]
l
60
[-1.46890601e-04  2.97012753e-01  2.23459551e+02]
[-1.44144751e-04  3.79892991e-01  8.20776571e+02]
l
61
[-1.58316863e-04  2.99219103e-01  2.25029709e+02]
[-1.23144888e-04  3.60671477e-01  8.22252646e+02]
l
62
[-1.81197267e-04  3.11669585e-01  2.25642574e+02]
[-1.47440303e-04  3.85097839e-01  8.12591137e+02]
l
63
[-1.76767276e-04  3.06412339e-01  2.25403201e+02]
[-3.48882274e-05  2.82655481e-01  8.34943365e+02]
l
64
[-1.98480023e-04  3.18689472e-01  2.26046603e+02]
[-2.01126588e-04  4.17732162e-01  8.09340967e+02]
l
65
[-2.00814421e-04  3.17707993e-01  2.25304851e+02]
[-1.94837344e-04  3.98988842e-01  8.18394081e+02]
l
66
[-2.17014799e-04  3.26673567e-01  2.26120498e+02]
[-1.83658901e-04  3.90340447e-01  8.17812594e+02]
l
67
[-2.22689247e-04  3.29337742e-01  2.25539714e+02]
[-1.71558502e-04  3.72173369e-01  8.23065490e+02]
l
68
[-2.27377964e-04  3.30706449e-01  2.24228288e+02]
[-1.58453524e-04  3.50691068e-01  8.31273894e+02]
l
69
[-2.36745271e-04  3.34493666e-01  2.26193294e+02]
[-1.51669970e-04  3.52906956e-01  8.24892371e+02]
l
70
[-2.27553452e-04  3.29313178e-01  2.23884788e+02]
[-1.37502648e-04  3.25805362e-01  8.37358737e+02]
l
71
[-2.10263290e-04  3.18131598e-01  2.25423996e+02]
[-6.19480602e-05  2.84202765e-01  8.39309228e+02]
l
72
[-1.98769716e-04  3.12299170e-01  2.23871433e+02]
[-9.96646016e-05  2.91879335e-01  8.44563905e+02]
l
73
[-1.79649180e-04  3.01670344e-01  2.22804362e+02]
[-1.19151459e-04  2.99821774e-01  8.45891956e+02]
l
74
[-1.61253478e-04  2.87981751e-01  2.24279950e+02]
[-1.69884170e-04  3.32043165e-01  8.41448922e+02]
l
75
[-1.48025025e-04  2.82127861e-01  2.20619554e+02]
[-1.35956096e-04  2.94606329e-01  8.53574949e+02]
l
76
[-1.33668104e-04  2.68832635e-01  2.23460577e+02]
[-1.43380891e-04  3.01842090e-01  8.50367666e+02]
l
77
[-1.04729111e-04  2.51895983e-01  2.19658963e+02]
[-1.30369338e-04  2.69542121e-01  8.68584255e+02]
l
78
[-9.80273858e-05  2.45761515e-01  2.18976678e+02]
[-1.24551453e-04  2.54208129e-01  8.76536568e+02]
l
79
[-7.98209968e-05  2.29346700e-01  2.19883510e+02]
[-2.36856115e-04  3.50004115e-01  8.61456739e+02]
l
80
[-9.62538863e-05  2.37259441e-01  2.19186626e+02]
[-2.38984531e-04  3.52847750e-01  8.60011722e+02]
l
81
[-8.95405471e-05  2.24582167e-01  2.23182633e+02]
[-2.35552861e-04  3.59611019e-01  8.52971862e+02]
l
82
[-8.51855667e-05  2.18711486e-01  2.23023318e+02]
[-2.18553866e-04  3.39232941e-01  8.58905972e+02]
l
83
[-9.82177797e-05  2.23806708e-01  2.22528746e+02]
[-2.01614471e-04  3.27479207e-01  8.61032895e+02]
l
84
[-8.81811216e-05  2.17946874e-01  2.18543022e+02]
[-1.45000107e-04  2.78078693e-01  8.78439293e+02]
l
85
[-9.89549764e-05  2.20780305e-01  2.20307993e+02]
[-1.33514116e-04  2.71324617e-01  8.78608566e+02]
l
86
[-1.12907493e-04  2.25559768e-01  2.22139859e+02]
[-1.21140691e-04  2.57254632e-01  8.81240979e+02]
l
87
[-1.42476488e-04  2.36103777e-01  2.29748469e+02]
[-1.52333961e-04  3.00441436e-01  8.61586976e+02]
l
88
[-1.60325366e-04  2.43380230e-01  2.34116598e+02]
[-8.76361659e-05  2.62699872e-01  8.59037544e+02]
l
89
[-1.87414440e-04  2.59496912e-01  2.35812530e+02]
[-1.45309611e-04  3.08723121e-01  8.48241828e+02]
l
90
[-2.09750671e-04  2.71618232e-01  2.40228067e+02]
[-2.21421881e-04  3.87408599e-01  8.24409185e+02]
l
91
[-2.08914434e-04  2.73049897e-01  2.38388111e+02]
[-2.09092583e-04  3.64059334e-01  8.35994132e+02]
l
92
[-2.11321234e-04  2.77426109e-01  2.37504355e+02]
[-2.02315164e-04  3.54736521e-01  8.39572067e+02]
l
93
[-2.26247550e-04  2.82153916e-01  2.41521484e+02]
[-1.90596104e-04  3.49462204e-01  8.36419534e+02]
l
94
[-2.24813444e-04  2.82875886e-01  2.42431421e+02]
[-1.80114512e-04  3.42155766e-01  8.36296635e+02]
l
95
[-2.20709342e-04  2.83634186e-01  2.39774726e+02]
[-1.69792844e-04  3.22207666e-01  8.44417577e+02]
l
96
[-2.13652554e-04  2.81178391e-01  2.39694658e+02]
[-1.61651647e-04  3.09384207e-01  8.48666809e+02]
l
97
[-2.10082198e-04  2.80945413e-01  2.39511951e+02]
[-1.87034294e-04  3.26293672e-01  8.46073338e+02]
l
98
[-2.02001639e-04  2.78803405e-01  2.37506795e+02]
[-1.43224617e-04  2.85234418e-01  8.59054526e+02]
l
99
[-1.94945592e-04  2.74210719e-01  2.39698557e+02]
[-1.83181302e-04  3.17283153e-01  8.51571089e+02]
l
100
[-1.93845282e-04  2.74724801e-01  2.38578957e+02]
[-1.81896511e-04  3.09256230e-01  8.57156756e+02]
l
101
[-1.96562837e-04  2.77748357e-01  2.39012700e+02]
[-2.08882049e-04  3.30014324e-01  8.53923718e+02]
l
102
[-1.97627707e-04  2.77435240e-01  2.40943936e+02]
[-2.30470960e-04  3.50932291e-01  8.47939563e+02]
l
103
[-2.02742206e-04  2.82132337e-01  2.40449109e+02]
[-2.31778353e-04  3.50922244e-01  8.50331210e+02]
l
104
[-2.07547801e-04  2.85354312e-01  2.41433626e+02]
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700
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701
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702
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703
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704
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705
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706
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707
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708
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709
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710
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711
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712
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713
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714
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715
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716
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717
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718
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719
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720
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721
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722
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723
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724
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725
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726
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727
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728
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729
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730
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731
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732
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733
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734
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735
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736
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737
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738
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739
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740
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741
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742
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743
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744
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745
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746
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747
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748
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749
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750
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751
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752
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753
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754
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755
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756
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757
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758
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759
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760
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761
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762
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763
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764
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765
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766
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767
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768
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769
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770
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771
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772
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773
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774
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775
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776
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777
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778
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779
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780
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781
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782
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783
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784
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785
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786
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787
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788
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789
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790
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791
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792
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793
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794
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795
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796
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797
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798
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799
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800
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801
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802
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803
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804
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805
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806
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807
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808
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l
809
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l
810
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l
811
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l
812
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l
813
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l
814
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l
815
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l
816
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[ 2.73787924e-04 -2.55609699e-01  1.08889056e+03]
l
817
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[ 2.79692008e-04 -2.55483555e-01  1.07871189e+03]
l
818
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819
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820
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821
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822
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823
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824
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825
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826
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827
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828
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829
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830
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831
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832
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833
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834
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835
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836
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837
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838
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839
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840
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841
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842
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843
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844
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845
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846
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847
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848
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849
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850
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851
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852
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853
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854
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855
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856
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857
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858
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859
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860
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861
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862
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863
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864
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865
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866
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867
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868
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869
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870
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871
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872
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873
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874
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875
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876
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877
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878
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879
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880
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881
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882
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883
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884
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885
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886
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887
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888
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889
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890
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891
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892
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893
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894
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895
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896
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897
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898
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899
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900
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901
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902
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903
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l
904
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905
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906
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907
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908
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909
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l
910
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l
911
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l
912
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l
913
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914
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915
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916
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917
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918
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919
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l
920
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l
921
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l
922
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923
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l
924
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l
925
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[ 1.74646127e-04 -3.13657378e-01  1.11809039e+03]
l
926
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l
927
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l
928
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[ 1.23859839e-04 -2.63278262e-01  1.10949167e+03]
l
929
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[ 1.21754503e-04 -2.68322725e-01  1.11409829e+03]
l
930
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[ 1.55168006e-04 -2.85472412e-01  1.11402063e+03]
l
931
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[ 1.69319933e-04 -2.92295423e-01  1.11349477e+03]
l
932
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[ 1.75378955e-04 -2.97263907e-01  1.11531218e+03]
l
933
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[ 2.01104512e-04 -3.14920454e-01  1.11673806e+03]
l
934
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[ 2.08654602e-04 -3.25130324e-01  1.12185824e+03]
l
935
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[ 2.30402253e-04 -3.41355457e-01  1.12204010e+03]
l
936
[ 6.66727915e-05 -2.06611934e-01  4.33498516e+02]
[ 2.14308750e-04 -3.31013692e-01  1.12097764e+03]
l
937
[ 5.97879750e-05 -1.94991479e-01  4.29707498e+02]
[ 2.42406919e-04 -3.62565997e-01  1.12983416e+03]
l
938
[ 7.23215685e-05 -2.09170143e-01  4.35102516e+02]
[ 2.19326031e-04 -3.41434380e-01  1.12521157e+03]
l
939
[ 7.78471515e-05 -2.07676538e-01  4.32649512e+02]
[ 2.17344104e-04 -3.45680965e-01  1.12811106e+03]
l
940
[ 1.34315928e-04 -2.61795845e-01  4.44971968e+02]
[ 2.28915510e-04 -3.58614792e-01  1.13050723e+03]
l
941
[ 1.30351927e-04 -2.52145733e-01  4.41190796e+02]
[ 2.50528693e-04 -3.68844525e-01  1.13141231e+03]
l
942
[ 1.46257910e-04 -2.60887188e-01  4.39785207e+02]
[ 2.52590659e-04 -3.76275406e-01  1.13524103e+03]
l
943
[ 1.73559076e-04 -2.88940938e-01  4.47738447e+02]
[ 2.70860655e-04 -3.91621467e-01  1.13635027e+03]
l
944
[ 1.66540289e-04 -2.73311121e-01  4.37126088e+02]
[ 2.48682153e-04 -3.91535900e-01  1.14563570e+03]
l
945
[ 1.82426829e-04 -2.96062667e-01  4.46446398e+02]
[ 2.48153060e-04 -3.92226229e-01  1.14345699e+03]
l
946
[ 1.66987245e-04 -2.82246152e-01  4.43999164e+02]
[ 1.99530562e-04 -3.60096463e-01  1.14125587e+03]
l
947
[ 1.68932207e-04 -2.79546817e-01  4.39748486e+02]
[ 2.04513712e-04 -3.70039227e-01  1.14784877e+03]
l
948
[ 1.58346751e-04 -2.80484397e-01  4.46119406e+02]
[ 1.96893481e-04 -3.59394921e-01  1.14313178e+03]
l
949
[ 1.53303936e-04 -2.73924916e-01  4.42196762e+02]
[ 1.93249810e-04 -3.63065922e-01  1.14890695e+03]
l
950
[ 1.58163983e-04 -2.82388941e-01  4.45263381e+02]
[ 1.46081453e-04 -3.19101853e-01  1.14025488e+03]
l
951
[ 1.29514783e-04 -2.56612972e-01  4.39075412e+02]
[ 1.46840619e-04 -3.23555466e-01  1.14386842e+03]
l
952
[ 1.14637724e-04 -2.32537552e-01  4.26949361e+02]
[ 1.38239828e-04 -3.25585824e-01  1.15206711e+03]
l
953
[ 1.07833058e-04 -2.41854115e-01  4.37114521e+02]
[ 1.49518097e-04 -3.25519940e-01  1.14694878e+03]
l
954
[ 8.81210978e-05 -2.14475833e-01  4.26504584e+02]
[ 1.53398858e-04 -3.33419315e-01  1.15426257e+03]
l
955
[ 7.45336855e-05 -2.11296476e-01  4.30667085e+02]
[ 1.32493365e-04 -3.20777388e-01  1.15195062e+03]
l
956
[ 9.63985234e-05 -2.39690904e-01  4.42840131e+02]
[ 1.51045372e-04 -3.25533043e-01  1.14854924e+03]
l
957
[ 1.08881906e-04 -2.44544590e-01  4.41435563e+02]
[ 1.12776248e-04 -3.03801665e-01  1.15101474e+03]
l
958
[ 1.18883177e-04 -2.62902865e-01  4.52076976e+02]
[ 1.30229338e-04 -3.08983765e-01  1.14843420e+03]
l
959
[ 1.19636510e-04 -2.55576119e-01  4.47749829e+02]
[ 1.10567292e-04 -2.99428157e-01  1.15347744e+03]
l
960
[ 1.29150741e-04 -2.75781598e-01  4.60603133e+02]
[ 1.37258689e-04 -3.12752299e-01  1.15093192e+03]
l
961
[ 1.14727521e-04 -2.65353111e-01  4.61143383e+02]
[ 7.51353114e-05 -2.51489600e-01  1.13982901e+03]
l
962
[ 1.18148994e-04 -2.65796286e-01  4.61196557e+02]
[ 8.04067019e-05 -2.55954111e-01  1.14385620e+03]
l
963
[ 1.07593293e-04 -2.63144192e-01  4.66411975e+02]
[ 9.48823172e-05 -2.61319828e-01  1.14267275e+03]
l
964
[ 9.70424304e-05 -2.45680357e-01  4.60634344e+02]
[ 1.01547914e-04 -2.69476849e-01  1.14843370e+03]
l
965
[ 1.40845550e-04 -2.97756922e-01  4.78119427e+02]
[ 1.45832865e-04 -2.86974340e-01  1.14598877e+03]
l
966
[ 1.11638230e-04 -2.74636650e-01  4.77261683e+02]
[ 1.89645090e-04 -3.10502137e-01  1.14473225e+03]
l
967
[ 1.23602024e-04 -2.82989969e-01  4.78640190e+02]
[ 1.80331240e-04 -3.07711360e-01  1.14625485e+03]
l
968
[ 1.14406213e-04 -2.81781911e-01  4.83959413e+02]
[ 1.58267069e-04 -2.85872488e-01  1.13940893e+03]
l
969
[ 7.94071822e-05 -2.42138285e-01  4.73169871e+02]
[ 1.56098581e-04 -2.85432991e-01  1.14089520e+03]
l
970
[ 7.89167710e-05 -2.27667703e-01  4.63751739e+02]
[ 1.30072431e-04 -2.70094509e-01  1.14253629e+03]
l
971
[ 6.17902991e-05 -2.08772633e-01  4.60113715e+02]
[ 1.59373400e-04 -2.89424702e-01  1.14396468e+03]
l
972
[ 1.93970149e-05 -1.66518755e-01  4.49650716e+02]
[ 1.62780437e-04 -2.94091422e-01  1.14562456e+03]
l
973
[ 2.81249744e-05 -1.74198536e-01  4.53210083e+02]
[ 1.25267026e-04 -2.52574152e-01  1.13261038e+03]
l
974
[ 3.41830303e-05 -1.74147026e-01  4.50341710e+02]
[ 9.39402824e-05 -2.27133484e-01  1.13092758e+03]
l
975
[ 1.88308392e-05 -1.55661470e-01  4.46224469e+02]
[ 8.90782185e-05 -2.16462552e-01  1.12491344e+03]
l
976
[ 2.35738510e-06 -1.37433832e-01  4.43723270e+02]
[ 8.85331689e-05 -2.09745770e-01  1.11863694e+03]
l
977
[ 7.42053475e-06 -1.36605405e-01  4.41603616e+02]
[ 4.80778367e-05 -1.81042008e-01  1.11515375e+03]
l
978
[ 3.89293879e-05 -1.60853938e-01  4.48267068e+02]
[ 6.54593757e-05 -1.76082462e-01  1.10509931e+03]
l
979
[ 4.80831445e-05 -1.65990929e-01  4.49227880e+02]
[ 4.24341170e-05 -1.55648964e-01  1.10015025e+03]
l
980
[ 8.99222754e-05 -1.93564785e-01  4.51166905e+02]
[ 2.26269491e-05 -1.36177108e-01  1.09533944e+03]
l
981
[ 7.72959998e-05 -1.77998470e-01  4.46949597e+02]
[-5.40857441e-05 -7.30384147e-02  1.08354365e+03]
l
982
[ 9.18088084e-05 -1.73697615e-01  4.38815208e+02]
[-8.17128337e-05 -4.37138718e-02  1.07822664e+03]
l
983
[ 7.45813393e-05 -1.52647995e-01  4.32287796e+02]
[-4.04624193e-05 -6.69582251e-02  1.07892673e+03]
l
984
[ 7.62909626e-05 -1.33111358e-01  4.18066242e+02]
[-6.33236293e-05 -4.62415254e-02  1.07800577e+03]
l
985
[ 7.08758099e-05 -1.31520444e-01  4.17651085e+02]
[-8.27473870e-05 -1.29671327e-02  1.06562643e+03]
l
986
[ 6.17800611e-05 -1.15445644e-01  4.10144169e+02]
[ 1.65710584e-04 -2.44976892e-01  1.11045188e+03]
l
987
[ 6.09772298e-05 -1.15885856e-01  4.07900649e+02]
[ 1.41209851e-04 -2.19721866e-01  1.10443826e+03]
l
988
[ 9.37997374e-05 -1.51317804e-01  4.17207430e+02]
[ 2.05403981e-04 -2.56614714e-01  1.10261407e+03]
l
989
[ 1.55470316e-05 -7.00544721e-02  3.92179779e+02]
[ 1.52088197e-04 -2.46505809e-01  1.10982431e+03]
l
990
[ 1.13328969e-05 -6.10664810e-02  3.84327638e+02]
[ 1.74478931e-04 -2.61601221e-01  1.11399180e+03]
l
991
[ 4.95313431e-05 -9.18948113e-02  3.87979041e+02]
[ 2.01623953e-04 -2.75545210e-01  1.11297658e+03]
l
992
[ 1.01053676e-04 -1.21359531e-01  3.84883943e+02]
[ 1.88161653e-04 -2.71962074e-01  1.11589000e+03]
l
993
[ 1.01109102e-04 -1.25522608e-01  3.87498944e+02]
[ 2.00607812e-04 -2.75491900e-01  1.10792050e+03]
l
994
[ 1.24282114e-04 -1.43401594e-01  3.89097879e+02]
[ 2.26564175e-04 -2.94511363e-01  1.10728915e+03]
l
995
[ 2.23163236e-04 -2.46589401e-01  4.13821470e+02]
[ 2.16050921e-04 -2.86182811e-01  1.10126200e+03]
l
996
[ 2.56437699e-04 -2.70394364e-01  4.15353341e+02]
[ 2.27620059e-04 -2.97973699e-01  1.10167448e+03]
l
997
[ 2.83330200e-04 -2.84776188e-01  4.10109947e+02]
[ 2.36989034e-04 -3.15755204e-01  1.11048591e+03]
l
998
[ 2.45898686e-04 -2.58999888e-01  4.03895494e+02]
[ 1.81361210e-04 -2.71339732e-01  1.10240938e+03]
l
999
[ 2.37622520e-04 -2.46504572e-01  3.93720809e+02]
[ 1.04114675e-04 -2.18211875e-01  1.10028970e+03]
l
1000
[ 1.85158010e-04 -2.10984542e-01  3.87280161e+02]
[ 5.25770448e-05 -1.68033615e-01  1.08750951e+03]
l
1001
[ 1.29930432e-04 -1.58668209e-01  3.70049986e+02]
[ 1.36297785e-04 -2.42334374e-01  1.10394380e+03]
l
1002
[ 1.62310631e-04 -1.69972740e-01  3.54574185e+02]
[ 1.36638032e-04 -2.66833796e-01  1.12476562e+03]
l
1003
[ 9.36399808e-05 -8.86880172e-02  3.21579753e+02]
[ 1.80446956e-04 -3.20001100e-01  1.14685524e+03]
l
1004
[ 8.26884650e-05 -4.88144631e-02  2.93313818e+02]
[ 1.29734897e-04 -2.93715396e-01  1.15395900e+03]
l
1005
[ 6.45061991e-05 -4.94849914e-02  3.00610948e+02]
[ 1.94867260e-04 -3.16411369e-01  1.15143875e+03]
l
1006
[ 6.93921369e-05 -1.31547259e-01  3.66367344e+02]
[ 2.85485968e-04 -3.43552392e-01  1.12117477e+03]
l
1007
[ 8.53996966e-05 -1.36768742e-01  3.64178564e+02]
[ 2.99793286e-04 -3.63487399e-01  1.13060588e+03]
l
1008
[ 1.31970336e-04 -2.21753541e-01  4.03942594e+02]
[ 3.69637034e-04 -4.07276292e-01  1.11988539e+03]
l
1009
[ 1.09739159e-04 -1.87581142e-01  3.88597349e+02]
[ 3.40538011e-04 -4.03145101e-01  1.12441216e+03]
l
1010
[ 9.47555296e-05 -1.58880270e-01  3.69745003e+02]
[ 3.07389047e-04 -3.95206816e-01  1.12881759e+03]
l
1011
[ 5.19099223e-05 -1.20615554e-01  3.61992327e+02]
[ 2.80502651e-04 -3.75353393e-01  1.12002275e+03]
l
1012
[ 3.43078677e-05 -6.40575643e-02  3.23405385e+02]
[ 2.62680480e-04 -3.88881129e-01  1.13883499e+03]
l
1013
[ 3.88599148e-05 -6.53746280e-02  3.18587206e+02]
[ 2.86683575e-04 -4.15721466e-01  1.14281271e+03]
l
1014
[ 5.15464867e-05 -7.10802375e-02  3.18296907e+02]
[ 3.28991212e-04 -4.57184658e-01  1.15143417e+03]
l
1015
[ 5.26954862e-05 -6.34167728e-02  3.10634635e+02]
[ 1.90443229e-04 -3.12768224e-01  1.11765871e+03]
l
1016
[ 9.01094631e-05 -9.54950534e-02  3.17399802e+02]
[ 2.04137389e-04 -3.23080059e-01  1.11821406e+03]
l
1017
[ 1.15936698e-04 -1.20677414e-01  3.21910980e+02]
[ 1.50883533e-04 -3.02943595e-01  1.11967392e+03]
l
1018
[ 1.46437650e-04 -1.69298736e-01  3.41328388e+02]
[ 1.42239841e-04 -2.95559179e-01  1.11542629e+03]
l
1019
[ 7.47875232e-05 -1.02156223e-01  3.25323954e+02]
[ 1.39725545e-04 -2.93092944e-01  1.11667241e+03]
l
1020
[ 1.19391173e-04 -1.35912806e-01  3.27650766e+02]
[ 1.41292600e-04 -2.99025023e-01  1.12339071e+03]
l
1021
[ 1.26650629e-04 -1.64755902e-01  3.47579037e+02]
[ 1.68566128e-04 -3.06985849e-01  1.11509856e+03]
l
1022
[ 1.61519585e-04 -1.82474694e-01  3.41759467e+02]
[ 1.57469304e-04 -3.07566110e-01  1.12177485e+03]
l
1023
[ 1.48811416e-04 -1.69792490e-01  3.31632011e+02]
[ 1.44008133e-04 -3.05648682e-01  1.12768325e+03]
l
1024
[ 1.54475774e-04 -1.71564145e-01  3.26918657e+02]
[ 1.29522977e-04 -2.93675339e-01  1.12722832e+03]
l
1025
[ 1.05909058e-04 -1.17124261e-01  3.08345133e+02]
[ 1.59612035e-04 -3.27428182e-01  1.13844366e+03]
l
1026
[ 1.49569416e-04 -1.81385134e-01  3.35141801e+02]
[ 1.03045559e-04 -2.42168059e-01  1.10309489e+03]
l
1027
[ 1.30613031e-04 -1.84402290e-01  3.45624042e+02]
[ 1.44623670e-04 -2.54513288e-01  1.09389597e+03]
l
1028
[ 1.12659700e-04 -1.86201427e-01  3.56525788e+02]
[ 2.08103816e-04 -2.90343344e-01  1.09131217e+03]
l
1029
[ 1.66678008e-04 -2.23734432e-01  3.61562678e+02]
[ 1.29224243e-04 -2.31551937e-01  1.07885246e+03]
l
1030
[ 2.67272219e-04 -3.20149826e-01  3.79516157e+02]
[ 5.24995327e-05 -1.75349535e-01  1.07267395e+03]
l
1031
[ 2.40799046e-04 -3.30245217e-01  3.96837919e+02]
[ 1.40379711e-04 -2.29665978e-01  1.07263543e+03]
l
1032
[ 2.10151866e-04 -3.16798957e-01  3.97950089e+02]
[-9.50848826e-05 -2.95696977e-02  1.03602213e+03]
l
1033
[ 2.29277541e-04 -3.21426431e-01  3.89709138e+02]
[-1.51891387e-04  3.31539191e-02  1.02433440e+03]
l
1034
[ 2.79999045e-04 -3.57062806e-01  3.97909934e+02]
[ 4.44095789e-05 -1.21513674e-01  1.04354013e+03]
l
1035
[ 2.66752174e-04 -3.54932094e-01  4.04018507e+02]
[ 3.09594119e-04 -3.61548349e-01  1.08718775e+03]
l
1036
[ 3.20460457e-04 -4.16257511e-01  4.26713335e+02]
[ 3.59407804e-04 -4.10177392e-01  1.09341838e+03]
l
1037
[ 2.56102245e-04 -3.75312450e-01  4.25563090e+02]
[ 3.74834901e-04 -4.33403767e-01  1.10235884e+03]
l
1038
[ 1.95948871e-04 -3.26122790e-01  4.17993776e+02]
[ 5.72157816e-04 -5.51268008e-01  1.11722977e+03]
l
1039
[ 2.53087552e-04 -3.92336331e-01  4.39760937e+02]
[ 6.10607060e-04 -5.81467518e-01  1.12288966e+03]
l
1040
[ 1.76445401e-04 -3.04989723e-01  4.16082682e+02]
[ 6.40066735e-04 -6.11672024e-01  1.13610898e+03]
l
1041
[ 1.51015948e-04 -2.82769177e-01  4.08617446e+02]
[ 5.54697428e-04 -5.83744658e-01  1.14201759e+03]
l
1042
[ 2.16417358e-04 -3.78698486e-01  4.40204253e+02]
[ 3.45191699e-04 -4.45003670e-01  1.12086084e+03]
l
1043
[ 1.20478990e-04 -2.79904291e-01  4.15933124e+02]
[ 3.58382226e-04 -4.74752875e-01  1.13329693e+03]
l
1044
[ 1.48366832e-04 -2.98195956e-01  4.18925363e+02]
[ 3.54370325e-04 -4.86996554e-01  1.14037872e+03]
l
1045
[ 1.87920109e-04 -2.85073232e-01  3.99032246e+02]
[ 4.34048670e-04 -6.02333040e-01  1.17948859e+03]
l
1046
[ 2.7368537e-04 -3.8903250e-01  4.3436668e+02]
[ 2.76709058e-04 -4.35003268e-01  1.13298773e+03]
l
1047
[ 3.22984941e-04 -4.01171714e-01  4.27770325e+02]
[ 1.82748684e-04 -3.70184219e-01  1.12685225e+03]
l
1048
[ 5.05010887e-04 -4.97448833e-01  4.18276815e+02]
[ 1.03117206e-04 -3.51070926e-01  1.14404043e+03]
l
1049
[ 4.47448148e-04 -4.33616042e-01  3.95734441e+02]
[-2.13001107e-05 -2.79605294e-01  1.14114833e+03]
l
1050
[ 4.08958957e-04 -4.07059290e-01  3.84555257e+02]
[-8.80796425e-05 -2.42423230e-01  1.14332710e+03]
l
1051
[ 2.91005440e-04 -3.72526540e-01  4.03240682e+02]
[-1.30073401e-04 -1.91543001e-01  1.12745239e+03]
l
1052
[ 2.28221492e-04 -3.37649578e-01  4.01170997e+02]
[-2.24745075e-04 -1.16466924e-01  1.11683382e+03]
l
1053
[ 2.28139762e-04 -3.31237363e-01  3.96162093e+02]
[-4.89551843e-04  7.01966051e-02  1.10163349e+03]
l
1054
[ 1.19777061e-04 -3.11742044e-01  4.31091115e+02]
[ 4.25485705e-05 -2.45357960e-01  1.11557347e+03]
l
1055
[ 3.41311163e-05 -2.65099767e-01  4.41034940e+02]
[ 1.16770720e-04 -2.82361220e-01  1.11374965e+03]
l
1056
[ 6.29505106e-05 -3.04282298e-01  4.59733873e+02]
[ 1.06992691e-04 -2.63249779e-01  1.10545075e+03]
l
1057
[ 4.84505236e-05 -3.04637127e-01  4.71727194e+02]
[ 1.23265371e-04 -2.66160350e-01  1.09904182e+03]
l
1058
[ 5.33783757e-05 -3.03836049e-01  4.73310308e+02]
[ 1.37772293e-04 -2.73503941e-01  1.09822398e+03]
l
1059
[ 6.12461636e-05 -3.20126224e-01  4.88584901e+02]
[ 1.55271375e-04 -2.72650263e-01  1.08845579e+03]
l
1060
[ 1.24083384e-04 -3.76155949e-01  5.08371894e+02]
[ 2.20723384e-04 -3.02210672e-01  1.08414471e+03]
l
1061
[ 1.41792659e-04 -4.01483570e-01  5.25538156e+02]
[ 2.91220054e-04 -3.41461795e-01  1.08018821e+03]
l
1062
[ 1.47035252e-04 -3.90236940e-01  5.20132392e+02]
[ 2.99269585e-04 -3.55337121e-01  1.08602925e+03]
l
1063
[ 1.37793663e-04 -3.32153349e-01  4.83398500e+02]
[ 2.48620713e-04 -3.40215530e-01  1.10353835e+03]
l
1064
[ 1.24236926e-04 -2.94543665e-01  4.63873149e+02]
[ 1.88860691e-04 -3.09565290e-01  1.10993571e+03]
l
1065
[ 1.13584708e-04 -2.54010989e-01  4.38492836e+02]
[ 2.08360307e-04 -3.41795082e-01  1.12890983e+03]
l
1066
[ 1.03067185e-04 -2.24344851e-01  4.21530096e+02]
[ 1.78450757e-04 -3.33829924e-01  1.13778487e+03]
l
1067
[ 9.65312518e-05 -2.24767325e-01  4.26305913e+02]
[ 2.27936282e-04 -3.72467159e-01  1.14152220e+03]
l
1068
[ 9.01601428e-05 -2.13558237e-01  4.22245407e+02]
[ 2.42149391e-04 -3.94902779e-01  1.15030785e+03]
l
1069
[ 7.08705359e-05 -2.11172911e-01  4.30888095e+02]
[ 2.72208747e-04 -4.16535296e-01  1.15018443e+03]
l
1070
[ 5.92485708e-05 -2.06668734e-01  4.33167178e+02]
[ 2.01902422e-04 -3.46273612e-01  1.13470554e+03]
l
1071
[ 4.22335760e-05 -1.79267809e-01  4.20996720e+02]
[ 1.92216862e-04 -3.48163402e-01  1.14272011e+03]
l
1072
[ 1.68888485e-05 -1.63622747e-01  4.23749131e+02]
[ 1.79266984e-04 -3.39487837e-01  1.13948974e+03]
l
1073
[ 2.08154549e-05 -1.58402356e-01  4.19654066e+02]
[ 1.58874057e-04 -3.32947725e-01  1.14447582e+03]
l
1074
[ 6.26043636e-06 -1.48089441e-01  4.20442308e+02]
[ 1.67733191e-04 -3.36380090e-01  1.14412753e+03]
l
1075
[ 1.82833243e-05 -1.61770393e-01  4.27168121e+02]
[ 1.70848050e-04 -3.39458271e-01  1.14470238e+03]
l
1076
[ 3.03575875e-05 -1.65015934e-01  4.25495019e+02]
[ 9.47943325e-05 -2.91116720e-01  1.14367303e+03]
l
1077
[ 7.44110883e-05 -2.20970313e-01  4.50308513e+02]
[ 1.79828283e-04 -3.39749439e-01  1.14068623e+03]
l
1078
[ 8.86395567e-05 -2.33354390e-01  4.55893872e+02]
[ 1.65686723e-04 -3.29694082e-01  1.14080804e+03]
l
1079
[ 7.73676136e-05 -2.19546126e-01  4.51871307e+02]
[ 1.49946700e-04 -3.20527884e-01  1.14405023e+03]
l
1080
[ 8.26650402e-05 -2.16856691e-01  4.49464435e+02]
[ 1.69098196e-04 -3.43570677e-01  1.15338966e+03]
l
1081
[ 1.01627713e-04 -2.12609919e-01  4.36988660e+02]
[ 1.44165086e-04 -3.39117663e-01  1.16609504e+03]
l
1082
[ 9.73760095e-05 -2.15734069e-01  4.41730262e+02]
[ 1.13985805e-04 -3.01892361e-01  1.15518034e+03]
l
1083
[ 1.20432153e-04 -2.32004648e-01  4.43604265e+02]
[ 1.34966003e-04 -3.18286425e-01  1.15975271e+03]
l
1084
[ 1.18264312e-04 -2.26764676e-01  4.39940338e+02]
[ 1.56130807e-04 -3.29694528e-01  1.16389850e+03]
l
1085
[ 1.09162768e-04 -2.24504340e-01  4.42707920e+02]
[ 1.76118797e-04 -3.40155256e-01  1.16290953e+03]
l
1086
[ 1.18544664e-04 -2.16052584e-01  4.30921857e+02]
[ 1.61863114e-04 -3.42771878e-01  1.17197456e+03]
l
1087
[ 1.10086616e-04 -2.21979715e-01  4.39057396e+02]
[ 1.69342771e-04 -3.38692292e-01  1.16484629e+03]
l
1088
[ 1.21341419e-04 -2.37250991e-01  4.46346561e+02]
[ 1.40740688e-04 -3.12962227e-01  1.15680132e+03]
l
1089
[ 1.33140853e-04 -2.47597466e-01  4.49013142e+02]
[ 1.77584723e-04 -3.36516865e-01  1.15751157e+03]
l
1090
[ 1.31607471e-04 -2.58178718e-01  4.58455446e+02]
[ 2.08241442e-04 -3.50642247e-01  1.15081741e+03]
l
1091
[ 1.04579227e-04 -2.24587231e-01  4.46198835e+02]
[ 2.16090545e-04 -3.66488818e-01  1.15771293e+03]
l
1092
[ 1.12252978e-04 -2.22218416e-01  4.41628133e+02]
[ 2.19766056e-04 -3.67776250e-01  1.15757503e+03]
l
1093
[ 1.16552439e-04 -2.22468641e-01  4.40138150e+02]
[ 1.87340620e-04 -3.35650608e-01  1.15150948e+03]
l
1094
[ 1.01167164e-04 -1.97627270e-01  4.29377742e+02]
[ 1.73950233e-04 -3.27179059e-01  1.15231923e+03]
l
1095
[ 1.01345375e-04 -2.12120545e-01  4.40730148e+02]
[ 1.63156756e-04 -3.12779405e-01  1.14099687e+03]
l
1096
[ 9.63201285e-05 -2.09279935e-01  4.41775018e+02]
[ 1.68997285e-04 -3.13205042e-01  1.13873803e+03]
l
1097
[ 7.67688670e-05 -1.98102104e-01  4.43274818e+02]
[ 1.63949330e-04 -3.10122713e-01  1.13716318e+03]
l
1098
[ 7.14615342e-05 -1.98510203e-01  4.46663159e+02]
[ 1.69430976e-04 -3.09407398e-01  1.13501642e+03]
l
1099
[ 5.88139621e-05 -1.80674475e-01  4.41114615e+02]
[ 1.55676495e-04 -3.05480894e-01  1.13880507e+03]
l
1100
[ 5.00795160e-05 -1.73261725e-01  4.42051858e+02]
[ 7.60702245e-05 -2.40763176e-01  1.12852356e+03]
l
1101
[ 2.94362563e-05 -1.56969577e-01  4.41958274e+02]
[ 8.07281494e-05 -2.36867836e-01  1.12621345e+03]
l
1102
[ 1.89397740e-05 -1.20731451e-01  4.20518204e+02]
[ 1.06178396e-04 -2.79965846e-01  1.15180145e+03]
l
1103
[ 1.72063900e-05 -1.21020049e-01  4.24799065e+02]
[ 1.08472237e-04 -2.72559793e-01  1.14686603e+03]
l
1104
[ 1.79340440e-05 -1.17604922e-01  4.22487081e+02]
[ 6.96179241e-05 -2.40414177e-01  1.14539289e+03]
l
1105
[ 3.45468212e-05 -1.25558847e-01  4.22523749e+02]
[ 5.08171221e-05 -2.18292040e-01  1.14118819e+03]
l
1106
[ 5.54179118e-05 -1.52623018e-01  4.35112518e+02]
[ 9.00288179e-05 -2.31977863e-01  1.13309097e+03]
l
1107
[ 8.93650001e-05 -1.59551408e-01  4.25944063e+02]
[ 9.20062732e-05 -2.41599392e-01  1.14398594e+03]
l
1108
[ 1.32851983e-04 -2.07358391e-01  4.40777806e+02]
[ 1.50664163e-04 -2.64173088e-01  1.13672496e+03]
l
1109
[ 1.35175323e-04 -1.98559737e-01  4.33938427e+02]
[ 1.75677620e-04 -2.85907710e-01  1.14431731e+03]
l
1110
[ 1.38214431e-04 -2.07086283e-01  4.36642378e+02]
[ 1.81751763e-04 -2.85584492e-01  1.13940780e+03]
l
1111
[ 1.44981676e-04 -2.26000765e-01  4.46696045e+02]
[ 2.49140535e-04 -3.24583250e-01  1.13383875e+03]
l
1112
[ 1.39398445e-04 -2.11568441e-01  4.37503727e+02]
[ 2.35550711e-04 -3.24177986e-01  1.13673407e+03]
l
1113
[ 1.26350420e-04 -2.09231718e-01  4.41377966e+02]
[ 2.77263166e-04 -3.57395173e-01  1.13521066e+03]
l
1114
[ 1.10937988e-04 -1.92252910e-01  4.34987276e+02]
[ 2.75468102e-04 -3.64800084e-01  1.13768031e+03]
l
1115
[ 1.02855650e-04 -1.76817804e-01  4.25370325e+02]
[ 2.75075866e-04 -3.72140118e-01  1.14228263e+03]
l
1116
[ 8.76869860e-05 -1.82343605e-01  4.37437676e+02]
[ 2.56576782e-04 -3.37519673e-01  1.12248096e+03]
l
1117
[ 7.45989018e-05 -1.75221299e-01  4.38869617e+02]
[ 2.63903253e-04 -3.46947673e-01  1.12138945e+03]
l
1118
[ 5.04323416e-05 -1.54302855e-01  4.34655874e+02]
[ 2.42710786e-04 -3.40147517e-01  1.12171768e+03]
l
1119
[ 5.54939950e-05 -1.51622584e-01  4.30148192e+02]
[ 2.50060375e-04 -3.52134122e-01  1.12702953e+03]
l
1120
[ 4.05079397e-05 -1.27797080e-01  4.18649076e+02]
[ 2.51991181e-04 -3.63846473e-01  1.13630823e+03]
l
1121
[ 3.24711192e-05 -1.10489581e-01  4.09978546e+02]
[ 2.64955937e-04 -3.80987822e-01  1.14402680e+03]
l
1122
[ 4.06674268e-05 -1.06642872e-01  4.02490932e+02]
[ 2.21043993e-04 -3.66450764e-01  1.15168620e+03]
l
1123
[ 5.23446671e-05 -1.01441182e-01  3.92658004e+02]
[ 1.94238403e-04 -3.59526357e-01  1.16074434e+03]
l
1124
[ 5.36159441e-05 -1.14428174e-01  4.02274489e+02]
[ 1.59946530e-04 -3.22841464e-01  1.14875272e+03]
l
1125
[ 5.66325474e-05 -1.02549934e-01  3.91962587e+02]
[ 1.67706647e-04 -3.40428873e-01  1.16027879e+03]
l
1126
[ 8.34152075e-05 -1.30127780e-01  3.98837740e+02]
[ 1.79958360e-04 -3.47854497e-01  1.16092272e+03]
l
1127
[ 9.02459972e-05 -1.45636851e-01  4.06547961e+02]
[ 1.75777732e-04 -3.41221749e-01  1.15627655e+03]
l
1128
[ 1.10514646e-04 -1.57903310e-01  4.04552319e+02]
[ 1.75727804e-04 -3.50012193e-01  1.16420005e+03]
l
1129
[ 1.46355574e-04 -2.01606032e-01  4.20712118e+02]
[ 1.29152549e-04 -2.96937043e-01  1.14448980e+03]
l
1130
[ 1.70475754e-04 -2.29214762e-01  4.29153008e+02]
[ 1.41071031e-04 -2.95927113e-01  1.14000332e+03]
l
1131
[ 1.74558841e-04 -2.36581469e-01  4.30292109e+02]
[ 1.39935901e-04 -2.95576691e-01  1.14074551e+03]
l
1132
[ 1.65952574e-04 -2.51924591e-01  4.44630464e+02]
[ 1.82973823e-04 -3.09058181e-01  1.13004450e+03]
l
1133
[ 1.58008774e-04 -2.31939206e-01  4.30631164e+02]
[ 1.78761895e-04 -3.20661056e-01  1.14132884e+03]
l
1134
[ 1.44985309e-04 -2.25291813e-01  4.30499187e+02]
[ 1.40525962e-04 -2.88194399e-01  1.13220343e+03]
l
1135
[ 1.34867087e-04 -2.22415247e-01  4.31230327e+02]
[ 1.43054090e-04 -2.86865764e-01  1.12995209e+03]
l
1136
[ 1.04023253e-04 -2.02212809e-01  4.29940890e+02]
[ 1.54885575e-04 -2.90878360e-01  1.12727013e+03]
l
1137
[ 1.00714668e-04 -2.12286037e-01  4.39357589e+02]
[ 1.63932815e-04 -2.87163850e-01  1.11973820e+03]
l
1138
[ 6.88627834e-05 -1.80507273e-01  4.31182017e+02]
[ 1.26047442e-04 -2.54382381e-01  1.11550331e+03]
l
1139
[ 6.01813814e-05 -1.77730914e-01  4.32389251e+02]
[ 1.36021390e-04 -2.56943734e-01  1.11295609e+03]
l
1140
[ 4.27288902e-05 -1.63834868e-01  4.31315628e+02]
[ 1.52679980e-04 -2.66462321e-01  1.11193069e+03]
l
1141
[ 4.92034389e-05 -1.53080886e-01  4.20412342e+02]
[ 1.44114442e-04 -2.68025063e-01  1.11941487e+03]
l
1142
[ 4.00732141e-05 -1.45814477e-01  4.20308930e+02]
[ 1.59097960e-04 -2.73464919e-01  1.11691492e+03]
l
1143
[ 5.39091092e-05 -1.54332802e-01  4.21468229e+02]
[ 1.73935918e-04 -2.81115704e-01  1.11599446e+03]
l
1144
[ 4.50373603e-05 -1.47585766e-01  4.20992789e+02]
[ 1.81725881e-04 -2.85446514e-01  1.11565778e+03]
l
1145
[ 5.48223102e-05 -1.63292074e-01  4.30593635e+02]
[ 1.99169770e-04 -2.90672825e-01  1.10886121e+03]
l
1146
[ 6.87662723e-05 -1.64179511e-01  4.24864231e+02]
[ 2.13826107e-04 -3.13058654e-01  1.11950371e+03]
l
1147
[ 6.24683583e-05 -1.57229036e-01  4.22913953e+02]
[ 2.05427093e-04 -3.06409607e-01  1.11713872e+03]
l
1148
[ 7.69999990e-05 -1.69900079e-01  4.26387227e+02]
[ 1.93110970e-04 -2.93627336e-01  1.11176192e+03]
l
1149
[ 7.76646567e-05 -1.54592051e-01  4.14122668e+02]
[ 2.18840134e-04 -3.33347194e-01  1.12974344e+03]
l
1150
[ 6.71572311e-05 -1.52982322e-01  4.17595485e+02]
[ 1.63320195e-04 -2.71299187e-01  1.11131746e+03]
l
1151
[ 6.95152633e-05 -1.50172111e-01  4.14834274e+02]
[ 1.67416523e-04 -2.81973949e-01  1.11487111e+03]
l
1152
[ 7.96776205e-05 -1.57372204e-01  4.14343774e+02]
[ 1.74658553e-04 -2.85361934e-01  1.11443586e+03]
l
1153
[ 7.55522442e-05 -1.53561989e-01  4.14039505e+02]
[ 1.92406929e-04 -2.98002854e-01  1.11492978e+03]
l
1154
[ 6.69673521e-05 -1.30926042e-01  3.99685709e+02]
[ 1.92128729e-04 -3.10969432e-01  1.12718983e+03]
l
1155
[ 7.48321877e-05 -1.43944742e-01  4.04650088e+02]
[ 2.09444076e-04 -3.21550132e-01  1.12615784e+03]
l
1156
[ 8.92523972e-05 -1.56177780e-01  4.06041865e+02]
[ 1.90288334e-04 -3.16584294e-01  1.12901254e+03]
l
1157
[ 7.11938336e-05 -1.25146612e-01  3.89949562e+02]
[ 1.58309187e-04 -3.06481932e-01  1.13848220e+03]
l
1158
[ 8.67526906e-05 -1.50536101e-01  4.00650533e+02]
[ 1.76556647e-04 -3.15730505e-01  1.13625722e+03]
l
1159
[ 9.86528646e-05 -1.64539907e-01  4.03983426e+02]
[ 2.06618849e-04 -3.46649113e-01  1.14442701e+03]
l
1160
[ 1.08433763e-04 -1.84740681e-01  4.13573101e+02]
[ 1.99954038e-04 -3.38005741e-01  1.13960120e+03]
l
1161
[ 1.19221906e-04 -2.13171763e-01  4.28532355e+02]
[ 1.78443487e-04 -3.07210138e-01  1.12621944e+03]
l
1162
[ 1.14328986e-04 -2.00712679e-01  4.20635815e+02]
[ 1.85227050e-04 -3.24952978e-01  1.13589746e+03]
l
1163
[ 1.60495863e-04 -2.53926590e-01  4.37395644e+02]
[ 2.06195028e-04 -3.33283854e-01  1.13158370e+03]
l
1164
[ 1.77863735e-04 -2.71998741e-01  4.41460424e+02]
[ 2.05030791e-04 -3.38241866e-01  1.13293772e+03]
l
1165
[ 1.77749829e-04 -2.59698359e-01  4.30243690e+02]
[ 1.97427746e-04 -3.45110117e-01  1.14347904e+03]
l
1166
[ 1.83499915e-04 -2.70911057e-01  4.35393950e+02]
[ 2.23344077e-04 -3.63625254e-01  1.14447093e+03]
l
1167
[ 1.75506374e-04 -2.63831794e-01  4.32864294e+02]
[ 2.21181773e-04 -3.70033151e-01  1.14903575e+03]
l
1168
[ 1.63094907e-04 -2.64237826e-01  4.36397565e+02]
[ 2.33972235e-04 -3.81999972e-01  1.15160141e+03]
l
1169
[ 1.45393750e-04 -2.67976892e-01  4.46661571e+02]
[ 2.28716934e-04 -3.75678532e-01  1.14637367e+03]
l
1170
[ 1.28629604e-04 -2.54234724e-01  4.44221576e+02]
[ 2.59212694e-04 -4.08500298e-01  1.15554199e+03]
l
1171
[ 1.06278373e-04 -2.54366227e-01  4.54330017e+02]
[ 2.74274522e-04 -4.20439285e-01  1.15503990e+03]
l
1172
[ 7.35211730e-05 -2.27411199e-01  4.50835265e+02]
[ 1.93899565e-04 -3.45299044e-01  1.14024201e+03]
l
1173
[ 5.49942574e-05 -2.09961659e-01  4.46323587e+02]
[ 1.89287378e-04 -3.46232969e-01  1.14489644e+03]
l
1174
[ 6.86883452e-05 -2.28461111e-01  4.57090919e+02]
[ 1.70553316e-04 -3.30022418e-01  1.13837155e+03]
l
1175
[ 6.86382066e-05 -2.21083735e-01  4.53556942e+02]
[ 1.46620271e-04 -3.25169615e-01  1.14425022e+03]
l
1176
[ 5.42342618e-05 -2.05472606e-01  4.49926615e+02]
[ 1.51287180e-04 -3.29381012e-01  1.14848176e+03]
l
1177
[ 5.72135635e-05 -2.12254217e-01  4.56306462e+02]
[ 1.66141741e-04 -3.35645352e-01  1.14753407e+03]
l
1178
[ 5.91153364e-05 -2.01824507e-01  4.48953310e+02]
[ 1.51775405e-04 -3.34002259e-01  1.15608464e+03]
l
1179
[ 5.39511687e-05 -2.00070933e-01  4.52403499e+02]
[ 9.78147287e-05 -2.90090789e-01  1.14849691e+03]
l
1180
[ 5.38988940e-05 -1.99057887e-01  4.53405449e+02]
[ 1.32171577e-04 -3.15810312e-01  1.15335523e+03]
l
1181
[ 3.7784851e-05 -1.7705915e-01  4.4650239e+02]
[ 1.40758232e-04 -3.24702952e-01  1.15964297e+03]
l
1182
[ 3.86949937e-05 -1.82457902e-01  4.52585876e+02]
[ 1.36883026e-04 -3.14383872e-01  1.15619948e+03]
l
1183
[ 3.78714760e-05 -1.75359419e-01  4.49267798e+02]
[ 1.36068557e-04 -3.18652436e-01  1.16195942e+03]
l
1184
[ 1.43752082e-05 -1.55459828e-01  4.47509534e+02]
[ 1.45933872e-04 -3.22392824e-01  1.16225793e+03]
l
1185
[ 4.26659740e-05 -1.86191250e-01  4.59790784e+02]
[ 1.80090269e-04 -3.41217300e-01  1.16006867e+03]
l
1186
[ 5.42369298e-05 -1.83777479e-01  4.54167687e+02]
[ 1.98125021e-04 -3.54480883e-01  1.16813174e+03]
l
1187
[ 7.20568519e-05 -1.95724079e-01  4.56533347e+02]
[ 2.16151444e-04 -3.65258735e-01  1.16881512e+03]
l
1188
[ 9.47590885e-05 -2.13193891e-01  4.59969848e+02]
[ 2.44833311e-04 -3.85186748e-01  1.17068539e+03]
l
1189
[ 9.06156650e-05 -1.98621470e-01  4.50388672e+02]
[ 2.30645556e-04 -3.85474342e-01  1.17711408e+03]
l
1190
[ 1.24224704e-04 -2.30776088e-01  4.59492797e+02]
[ 2.07436624e-04 -3.63991028e-01  1.16902484e+03]
l
1191
[ 1.35216441e-04 -2.38579817e-01  4.60116823e+02]
[ 2.01707504e-04 -3.57366230e-01  1.16573219e+03]
l
1192
[ 1.59629540e-04 -2.62726271e-01  4.66871468e+02]
[ 1.99617322e-04 -3.46489147e-01  1.15745377e+03]
l
1193
[ 1.57202432e-04 -2.68152454e-01  4.72180043e+02]
[ 1.87706642e-04 -3.24577302e-01  1.14673598e+03]
l
1194
[ 1.69417235e-04 -2.68491493e-01  4.66168429e+02]
[ 1.62882618e-04 -3.10085493e-01  1.14947344e+03]
l
1195
[ 1.55200085e-04 -2.71828099e-01  4.74531030e+02]
[ 1.70219863e-04 -3.05498173e-01  1.14105509e+03]
l
1196
[ 1.52122523e-04 -2.73561924e-01  4.77447312e+02]
[ 1.73553494e-04 -3.08195430e-01  1.13867862e+03]
l
1197
[ 1.46681727e-04 -2.60513733e-01  4.70233823e+02]
[ 1.60598905e-04 -3.04752245e-01  1.14391206e+03]
l
1198
[ 1.38115620e-04 -2.44808508e-01  4.62783659e+02]
[ 1.79477193e-04 -3.19971093e-01  1.15156966e+03]
l
1199
[ 1.05151694e-04 -1.95818394e-01  4.40150685e+02]
[ 1.60677432e-04 -3.25341003e-01  1.16798003e+03]
l
1200
[ 1.07954867e-04 -1.82398904e-01  4.28249035e+02]
[ 1.44810881e-04 -3.25285886e-01  1.17938043e+03]
l
1201
[ 1.02722193e-04 -1.91187699e-01  4.37991248e+02]
[ 1.80719060e-04 -3.39963726e-01  1.17504305e+03]
l
1202
[ 8.39904348e-05 -1.74686745e-01  4.34272820e+02]
[ 1.83295832e-04 -3.47305587e-01  1.18128174e+03]
l
1203
[ 8.31730682e-05 -1.83053945e-01  4.42174377e+02]
[ 1.82571386e-04 -3.41628728e-01  1.17754000e+03]
l
1204
[ 1.12509793e-04 -2.18771198e-01  4.56554272e+02]
[ 2.18579332e-04 -3.67813035e-01  1.17809655e+03]
l
1205
[ 1.17648850e-04 -2.32624294e-01  4.65377645e+02]
[ 2.31344578e-04 -3.78214887e-01  1.17912895e+03]
l
1206
[ 1.47542665e-04 -2.76321327e-01  4.85869614e+02]
[ 2.25677717e-04 -3.59303981e-01  1.16684406e+03]
l
1207
[ 1.43788381e-04 -2.86302408e-01  4.95838158e+02]
[ 2.59486230e-04 -3.89673497e-01  1.16943477e+03]
l
1208
[ 1.71953317e-04 -3.08972361e-01  4.99966393e+02]
[ 2.49090644e-04 -3.90600737e-01  1.17480820e+03]
l
1209
[ 1.93995057e-04 -3.43804387e-01  5.16702176e+02]
[ 2.71084993e-04 -4.03783893e-01  1.17148609e+03]
l
1210
[ 1.79892382e-04 -3.21301169e-01  5.08174064e+02]
[ 2.50825959e-04 -4.05858251e-01  1.17951973e+03]
l
1211
[ 1.87556210e-04 -3.29844053e-01  5.11268439e+02]
[ 2.56233556e-04 -4.11175638e-01  1.18163215e+03]
l
1212
[ 1.89881821e-04 -3.32019589e-01  5.11435744e+02]
[ 2.28124381e-04 -4.01415501e-01  1.18491267e+03]
l
1213
[ 1.73693852e-04 -2.99165091e-01  4.94837402e+02]
[ 2.42696581e-04 -4.26699560e-01  1.20093247e+03]
l
1214
[ 1.64919335e-04 -2.99249404e-01  4.98817555e+02]
[ 2.44591829e-04 -4.28235984e-01  1.20138375e+03]
l
1215
[ 1.54102158e-04 -2.95847886e-01  5.00940603e+02]
[ 2.18497011e-04 -4.07763873e-01  1.19721603e+03]
l
1216
[ 1.39011201e-04 -2.75010109e-01  4.92004490e+02]
[ 2.39492068e-04 -4.35522496e-01  1.20946676e+03]
l
1217
[ 1.21082767e-04 -2.67884815e-01  4.95987400e+02]
[ 1.85331493e-04 -3.76316840e-01  1.19402058e+03]
l
1218
[ 1.02460926e-04 -2.53121002e-01  4.93252854e+02]
[ 1.74245828e-04 -3.70945747e-01  1.19623528e+03]
l
1219
[ 1.02066424e-04 -2.52217651e-01  4.93046758e+02]
[ 1.63876532e-04 -3.65173936e-01  1.19793096e+03]
l
1220
[ 1.11062869e-04 -2.65901820e-01  5.00999369e+02]
[ 1.44685876e-04 -3.51718424e-01  1.19343578e+03]
l
1221
[ 1.23251851e-04 -2.67577016e-01  4.97935423e+02]
[ 1.65291818e-04 -3.65334174e-01  1.20067478e+03]
l
1222
[ 1.13129815e-04 -2.64926169e-01  5.02702808e+02]
[ 1.93884438e-04 -3.76118486e-01  1.19695353e+03]
l
1223
[ 1.00060099e-04 -2.65183214e-01  5.10584505e+02]
[ 2.16900485e-04 -3.85506573e-01  1.19116368e+03]
l
1224
[ 9.85587950e-05 -2.55984479e-01  5.06229078e+02]
[ 2.24294677e-04 -3.94835527e-01  1.19583584e+03]
l
1225
[ 9.69409784e-05 -2.58544642e-01  5.10884191e+02]
[ 2.90233001e-04 -4.42182231e-01  1.19905910e+03]
l
1226
[ 9.07773603e-05 -2.37593201e-01  4.98398098e+02]
[ 2.83899593e-04 -4.52061423e-01  1.20953609e+03]
l
1227
[ 7.23004096e-05 -2.05281975e-01  4.82765482e+02]
[ 2.48171994e-04 -4.33713610e-01  1.21527964e+03]
l
1228
[ 8.02441721e-05 -2.04163868e-01  4.78749788e+02]
[ 2.65233444e-04 -4.50446275e-01  1.21896603e+03]
l
1229
[ 7.16962704e-05 -1.86105999e-01  4.67946445e+02]
[ 2.86451067e-04 -4.81365111e-01  1.23216623e+03]
l
1230
[ 7.09470975e-05 -1.80802510e-01  4.65621105e+02]
[ 1.77436631e-04 -3.60483805e-01  1.20129051e+03]
l
1231
[ 9.64119873e-05 -2.02447379e-01  4.71704285e+02]
[ 1.85418533e-04 -3.59255505e-01  1.19454160e+03]
l
1232
[ 9.19609533e-05 -1.94428323e-01  4.68688887e+02]
[ 1.57207673e-04 -3.38192701e-01  1.19149147e+03]
l
1233
[ 7.84151686e-05 -1.93188534e-01  4.76359156e+02]
[ 1.80972551e-04 -3.42204043e-01  1.18316721e+03]
l
1234
[ 6.89758530e-05 -1.82185354e-01  4.73085744e+02]
[ 1.89050774e-04 -3.46642146e-01  1.18379799e+03]
l
1235
[ 5.88702503e-05 -1.64930553e-01  4.65618129e+02]
[ 1.37270459e-04 -3.11866340e-01  1.18310935e+03]
l
1236
[ 4.76119347e-05 -1.53580885e-01  4.63007953e+02]
[ 7.34589176e-05 -2.56438347e-01  1.17392084e+03]
l
1237
[ 2.45885575e-05 -1.22489643e-01  4.51158530e+02]
[ 1.01317758e-04 -2.84787419e-01  1.18496035e+03]
l
1238
[ 1.96667601e-05 -1.16653003e-01  4.49443522e+02]
[ 1.13550547e-04 -2.89265525e-01  1.18530502e+03]
l
1239
[ 1.18563160e-05 -1.12118911e-01  4.51534982e+02]
[ 1.14720263e-04 -2.75139617e-01  1.17364983e+03]
l
1240
[-4.93552094e-06 -9.41785304e-02  4.46263663e+02]
[ 4.19921305e-05 -1.98715119e-01  1.15756753e+03]
l
1241
[-3.92339117e-06 -9.21154382e-02  4.46488325e+02]
[ 6.80239652e-05 -2.02515511e-01  1.15077080e+03]
l
1242
[ 1.33006938e-05 -1.03609117e-01  4.48177975e+02]
[ 1.08474541e-04 -2.12965906e-01  1.14635493e+03]
l
1243
[ 2.88137302e-05 -9.93285157e-02  4.38849204e+02]
[ 1.34912372e-04 -2.33778584e-01  1.15413627e+03]
l
1244
[ 3.53179465e-05 -1.03074655e-01  4.40970146e+02]
[ 1.44028798e-04 -2.27717968e-01  1.14459876e+03]
l
1245
[ 2.00790667e-05 -7.75024693e-02  4.30312659e+02]
[ 1.39240406e-04 -2.30455123e-01  1.14694200e+03]
l
1246
[ 2.94068462e-05 -7.76022452e-02  4.26957320e+02]
[ 1.76663286e-04 -2.52996519e-01  1.14875709e+03]
l
1247
[ 4.05702969e-05 -9.70127500e-02  4.38212888e+02]
[ 8.80182818e-05 -1.63636996e-01  1.11935060e+03]
l
1248
[ 3.62585883e-05 -8.32405421e-02  4.30658011e+02]
[ 6.74256967e-05 -1.50853318e-01  1.12114358e+03]
l
1249
[ 3.79489312e-05 -8.49576894e-02  4.31348017e+02]
[ 6.46057661e-05 -1.36824764e-01  1.11331884e+03]
l
1250
[ 2.50327534e-05 -7.82816294e-02  4.31877604e+02]
[ 5.44474754e-06 -6.88621174e-02  1.09174345e+03]
l
1251
[ 1.53840422e-05 -6.04104114e-02  4.20722891e+02]
[ 6.70633927e-06 -8.12493936e-02  1.10316970e+03]
l
1252
[ 1.48900160e-05 -5.39500119e-02  4.15693235e+02]
[ 2.18716469e-05 -8.48682905e-02  1.09979171e+03]
l
1253
[ 1.63293193e-05 -5.17670365e-02  4.12392740e+02]
[ 6.72901722e-05 -1.03030610e-01  1.09677029e+03]
l
1254
[ 3.23501532e-05 -5.17370073e-02  4.04510197e+02]
[ 9.40383234e-05 -1.21526626e-01  1.10048893e+03]
l
1255
[ 2.96550129e-05 -5.15911100e-02  4.05151237e+02]
[ 1.30501349e-04 -1.33899333e-01  1.09168717e+03]
l
1256
[ 2.50401823e-05 -4.24846249e-02  3.98536968e+02]
[ 1.13013770e-04 -1.23736389e-01  1.08842060e+03]
l
1257
[ 2.45757420e-05 -3.84141175e-02  3.94428466e+02]
[ 9.20630708e-05 -9.84872527e-02  1.07793606e+03]
l
1258
[ 1.97723659e-05 -3.49872545e-02  3.93762459e+02]
[ 8.69098607e-05 -8.27581414e-02  1.06449672e+03]
l
1259
[ 1.37415935e-05 -1.69912054e-02  3.81479170e+02]
[ 9.89131764e-05 -1.01455702e-01  1.07158257e+03]
l

'''
nx = 9 
ny = 6
ret, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)

src = np.float32([[580, 460], [700, 460], [1096, 720], [200, 720]])
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
'''

dist_pickle = pickle.load( open( "camera_cal/wide_dist_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]

video_input = 'challenge_video.mp4'
video_output = 'result_challenge_video1.mp4'

cap = cv2.VideoCapture(video_input)

fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(video_output, fourcc, 20.0, (1280, 720))

detected = False
left_fit = []
right_fit = []
ploty = []
i=0
while(True):
    ret, image = cap.read()
    #print(ret)
    if ret:
        i=i+1
        print(i)
        undistort_image = img_undistort(image, mtx, dist)
        #color_result_img,combined_binary_img = pipeline(undistort_image)
        #top_down_warped_img,Minv = warper(combined_binary_img)
        top_down_warped,Minv = warper(undistort_image)
        color_result_img,combined_binary_img = pipeline(top_down_warped)
        '''
        warp_image, M, Minv = warpImage(undistort_image, src, dst)
        hlsL_binary = hlsLSelect(warp_image)
        labB_binary = labBSelect(warp_image, (205, 255))
        combined_binary = np.zeros_like(sx_binary)
        combined_binary[(hlsL_binary == 1) | (labB_binary == 0)] = 1
        '''
        #print('detected = ',detected)
        #left_fit_warped,right_fit_warped,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = fit_polynomial(combined_binary_img)
        
        if detected == False:
            left_fit_warped,right_fit_warped,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = fit_polynomial(combined_binary_img)
            #out_img, left_fit, right_fit, ploty = fit_polynomial(combined_binary, nwindows=9, margin=80, minpix=40)
            #print(len(right_fit))
            #print(len(right_fit)>0 and len(left_fit)>0)
            print('k')
            if (len(left_fit_warped) > 0 and len(right_fit_warped) > 0) :
                detected = True
            else :
                #print('1')
                detected = False
        else:            
            left_fit_warped,right_fit_warped,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = search_around_poly(left_fit_warped,right_fit_warped,combined_binary_img)
            #track_result, left_fit, right_fit, ploty,  = search_around_poly(combined_binary, left_fit, right_fit)
            print('l')
            #print(left_fit_warped)
            #print(right_fit_warped)
            if (len(left_fit_warped) > 0 and len(right_fit_warped) > 0) :
                detected = True
            else :
                detected = False
        
        #result = drawing(undistort_image, combined_binary, warp_image, left_fitx, right_fitx)
        draw_result = drawing(undistort_image , combined_binary_img,Minv, left_fitx, right_fitx, ploty)
        out.write(draw_result)
    else:
        break
        
cap.release()
out.release()

1
[-6.47859555e-04  3.42364334e-01  4.49012964e+02]
[ 1.79144208e-04 -2.10889580e-01  1.02275596e+03]
k
2
[ 2.31629849e-04 -7.20106277e-01  7.64546633e+02]
[ 4.02121047e-04 -4.32629572e-01  1.07428598e+03]
l
3
[ 1.15432133e-04 -5.79091916e-01  7.23961327e+02]
[ 5.54105985e-04 -5.92268725e-01  1.11275320e+03]
l
4
[ 1.16775093e-04 -5.80274699e-01  7.23641269e+02]
[ 5.76939248e-04 -6.22933734e-01  1.12365484e+03]
l
5
[-1.05193329e-04 -3.13950068e-01  6.47450057e+02]
[ 5.72279315e-04 -6.32947898e-01  1.12939108e+03]
l
6
[-6.41144192e-05 -3.57434620e-01  6.60214449e+02]
[ 5.02715179e-04 -5.80958223e-01  1.12360944e+03]
l
7
[ 3.68718597e-04 -9.14977275e-01  8.39907701e+02]
[ 5.30850380e-04 -6.20335579e-01  1.13623354e+03]
l
8
[ 1.22098583e-04 -6.46606423e-01  7.70152418e+02]
[ 5.47742316e-04 -6.29739405e-01  1.13722326e+03]
l
9
[ 8.44903237e-05 -6.16472357e-01  7.68757652e+02]
[ 5.62447017e-04 -6.42513356e-01  1.13990384e+03]
l
10
[-2.14913509e-05 -5.05720837e-01  7.42773360e+02]
[ 5.91688934e-04 -6.70102023e-01  1.14774693e+03]
l
11
[-1.79546276e-04 -3.06603835e-01  6.81215531e+02]
[ 5.57222209e-04 -6.48338673e-01  1.14987429e+03]
l
12
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l

'''
nx = 9 
ny = 6
ret, mtx, dist, rvecs, tvecs = getCameraCalibrationCoefficients('camera_cal/calibration*.jpg', nx, ny)

src = np.float32([[580, 460], [700, 460], [1096, 720], [200, 720]])
dst = np.float32([[300, 0], [950, 0], [950, 720], [300, 720]])
'''

dist_pickle = pickle.load( open( "camera_cal/wide_dist_pickle.p", "rb" ) )
mtx = dist_pickle["mtx"]
dist = dist_pickle["dist"]

video_input = 'harder_challenge_video.mp4'
video_output = 'result_harder_challenge_video1.mp4'

cap = cv2.VideoCapture(video_input)

fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(video_output, fourcc, 20.0, (1280, 720))

detected = False
left_fit = []
right_fit = []
ploty = []
i=0
while(True):
    ret, image = cap.read()
    #print(ret)
    if ret:
        i=i+1
        print(i)
        undistort_image = img_undistort(image, mtx, dist)
        #color_result_img,combined_binary_img = pipeline(undistort_image)
        #top_down_warped_img,Minv = warper(combined_binary_img)
        top_down_warped,Minv = warper(undistort_image)
        color_result_img,combined_binary_img = pipeline(top_down_warped)
        '''
        warp_image, M, Minv = warpImage(undistort_image, src, dst)
        hlsL_binary = hlsLSelect(warp_image)
        labB_binary = labBSelect(warp_image, (205, 255))
        combined_binary = np.zeros_like(sx_binary)
        combined_binary[(hlsL_binary == 1) | (labB_binary == 0)] = 1
        '''
        #print('detected = ',detected)
        left_fit_warped,right_fit_warped,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = fit_polynomial(combined_binary_img)
        '''
        if detected == False:
            left_fit_warped,right_fit_warped,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = fit_polynomial(combined_binary_img)
            #out_img, left_fit, right_fit, ploty = fit_polynomial(combined_binary, nwindows=9, margin=80, minpix=40)
            #print(len(right_fit))
            #print(len(right_fit)>0 and len(left_fit)>0)
            print('k')
            if (len(left_fit_warped) > 0 and len(right_fit_warped) > 0) :
                detected = True
            else :
                #print('1')
                detected = False
        else:            
            left_fit_warped,right_fit_warped,warped_leftx, warped_lefty, warped_rightx, warped_righty,left_fitx, right_fitx,ploty,result = search_around_poly(left_fit_warped,right_fit_warped,combined_binary_img)
            #track_result, left_fit, right_fit, ploty,  = search_around_poly(combined_binary, left_fit, right_fit)
            print('l')
            #print(left_fit_warped)
            #print(right_fit_warped)
            if (len(left_fit_warped) > 0 and len(right_fit_warped) > 0) :
                detected = True
            else :
                detected = False
        '''
        #result = drawing(undistort_image, combined_binary, warp_image, left_fitx, right_fitx)
        draw_result = drawing(undistort_image , combined_binary_img,Minv, left_fitx, right_fitx, ploty)
        out.write(draw_result)
    else:
        break
        
cap.release()
out.release()

1
[-4.74353593e-04  6.66124519e-01  5.65575286e+01]
[-6.48640888e-04  7.55553262e-01  6.81672599e+02]
2
[-4.35426388e-04  6.44328208e-01  5.05178054e+01]
[-6.83173044e-04  7.59380208e-01  6.96117221e+02]
3
[-3.97257571e-04  6.18271676e-01  4.90881584e+01]
[-6.70628002e-04  7.41237237e-01  7.04702150e+02]
4
[-3.70718655e-04  6.03134859e-01  4.67330361e+01]
[-6.65278354e-04  7.37559477e-01  7.05604689e+02]
5
[-3.80901319e-04  6.22802006e-01  3.71310683e+01]
[-6.69224329e-04  7.47891572e-01  7.01347838e+02]
6
[-3.80725114e-04  6.33209594e-01  3.02233900e+01]
[-6.81522010e-04  7.64058165e-01  6.95735184e+02]
7
[-3.80417089e-04  6.38385784e-01  2.80457285e+01]
[-7.06643769e-04  8.10639748e-01  6.75709252e+02]
8
[-4.00199351e-04  6.55810554e-01  2.77388073e+01]
[-6.81337718e-04  8.34662666e-01  6.48540253e+02]
9
[-4.55638210e-04  7.12616396e-01  1.56225266e+01]
[-7.59736504e-04  9.46506543e-01  6.06024519e+02]
10
[-4.39648597e-04  7.01875842e-01  1.62494082e+01]
[-6.29070792e-04  8.45542392e-01  6.17425309e+02]
11
[-4.35315725e-04  7.09299839e-01  6.22620176e+00]
[-6.56530747e-04  8.70406559e-01  6.13667943e+02]
12
[-5.15324297e-04  7.81925854e-01 -1.28669767e+01]
[-7.15984301e-04  9.32212170e-01  5.98536413e+02]
13
[-4.40940407e-04  7.35601944e-01 -1.28242498e+01]
[-5.99560215e-04  8.21947787e-01  6.23649370e+02]
14
[-4.98893324e-04  7.82625777e-01 -2.27459888e+01]
[-5.51326421e-04  7.94909799e-01  6.20404501e+02]
15
[-5.12039555e-04  8.03945908e-01 -3.12332045e+01]
[-7.67024263e-05  3.57182996e-01  7.07790819e+02]
16
[-3.26534757e-04  6.06982378e-01  1.88804834e+01]
[-6.63574070e-06  3.10616607e-01  7.07449952e+02]
17
[-5.27672596e-04  8.39253049e-01 -4.54051443e+01]
[2.09295281e-04 1.09332591e-01 7.45039122e+02]
18
[-5.70092140e-04  8.89694150e-01 -6.07693366e+01]
[3.01205465e-04 2.44285323e-02 7.58963285e+02]
19
[-6.02531227e-04  9.25950536e-01 -7.03619835e+01]
[ 3.90971315e-04 -5.60134296e-02  7.69919643e+02]
20
[-6.31509698e-04  9.57823267e-01 -7.91689092e+01]
[ 4.87291635e-04 -1.40460623e-01  7.80367058e+02]
21
[-6.42791333e-04  9.75563349e-01 -8.88982610e+01]
[ 5.39225575e-04 -1.90488131e-01  7.89048333e+02]
22
[-5.89002370e-04  9.29033553e-01 -8.47502078e+01]
[ 5.18411671e-04 -1.63459868e-01  7.80209491e+02]
23
[-6.28310397e-04  9.87325989e-01 -1.09827999e+02]
[ 5.25300577e-04 -1.66702128e-01  7.79823611e+02]
24
[-5.62719242e-04  9.26370910e-01 -1.01392105e+02]
[ 5.72660328e-04 -2.11106486e-01  7.88506778e+02]
25
[-5.65816514e-04  9.39237149e-01 -1.12016500e+02]
[ 5.93848349e-04 -2.32137548e-01  7.91930165e+02]
26
[-6.20302476e-04  1.00549423e+00 -1.34153307e+02]
[ 5.50817335e-04 -1.84372924e-01  7.78718298e+02]
27
[-6.24521337e-04  1.02656364e+00 -1.48364356e+02]
[ 6.22240362e-04 -2.52971044e-01  7.95800120e+02]
28
[-6.74782853e-04  1.09019865e+00 -1.69864973e+02]
[2.82487949e-04 1.01829178e-01 7.08607334e+02]
29
[-6.95982233e-04  1.11286517e+00 -1.77403382e+02]
[2.10638388e-04 1.75366914e-01 6.92207518e+02]
30
[-7.12101069e-04  1.13154648e+00 -1.84099800e+02]
[2.19865542e-04 1.69185332e-01 6.95196122e+02]
31
[-7.15852332e-04  1.13350780e+00 -1.85519357e+02]
[2.68021869e-04 1.23276609e-01 7.09306251e+02]
32
[-7.16237600e-04  1.13454220e+00 -1.87539272e+02]
[3.13919944e-04 7.07416333e-02 7.27022675e+02]
33
[-7.58737341e-04  1.18274031e+00 -2.00599753e+02]
[3.65200244e-04 1.84532784e-02 7.43007535e+02]
34
[-7.64932169e-04  1.18424333e+00 -1.97880393e+02]
[2.82939081e-04 1.05252912e-01 7.22638378e+02]
35
[-7.02099530e-04  1.11502086e+00 -1.76600666e+02]
[3.39535312e-04 4.52817678e-02 7.36921841e+02]
36
[-1.85437272e-03  2.24141323e+00 -4.39976446e+02]
[ 4.52289459e-04 -6.34027072e-02  7.59660026e+02]
37
[-1.00849017e-03  1.44954261e+00 -2.56933632e+02]
[ 4.82925145e-04 -9.49168945e-02  7.66262821e+02]
38
[-1.14980078e-03  1.60097578e+00 -2.92107002e+02]
[ 5.19435869e-04 -1.18093992e-01  7.64946849e+02]
39
[-1.23200140e-03  1.69487402e+00 -3.14687181e+02]
[ 6.29041441e-04 -2.36112042e-01  7.93934916e+02]
40
[-1.40795205e-03  1.89071454e+00 -3.64425659e+02]
[ 7.21306257e-04 -3.22124072e-01  8.10496088e+02]
41
[-1.52457970e-03  2.02372111e+00 -3.98030793e+02]
[ 8.29446621e-04 -4.72888977e-01  8.58772539e+02]
42
[-2.65902388e-04  5.98721578e-01 -5.72382355e+00]
[ 8.99581340e-04 -5.55550068e-01  8.82137071e+02]
43
[-3.08979085e-05  3.30310516e-01  6.99600488e+01]
[ 9.42418767e-04 -6.04436054e-01  8.95238562e+02]
44
[-2.02077746e-05  3.14393349e-01  7.72562780e+01]
[ 1.00704845e-03 -6.86258934e-01  9.20012125e+02]
45
[-1.74323682e-03  2.31938331e+00 -4.90551905e+02]
[ 1.10645817e-03 -7.77354166e-01  9.37459233e+02]
46
[3.24020527e-05 2.66130343e-01 8.97755581e+01]
[ 1.18258829e-03 -8.41408393e-01  9.47980746e+02]
47
[-1.77333058e-03  2.37161064e+00 -5.10390628e+02]
[ 8.98849258e-04 -5.65582134e-01  8.86327527e+02]
48
[-1.85484862e-04  5.59086486e-01 -1.13736705e+00]
[ 9.00955787e-04 -5.73636674e-01  8.91344683e+02]
49
[-1.05693043e-03  1.56236312e+00 -2.85134389e+02]
[ 8.92639073e-04 -5.66543799e-01  8.90718670e+02]
50
[1.56815213e-04 1.66254620e-01 1.11162654e+02]
[ 8.56168708e-04 -5.17216718e-01  8.75713572e+02]
51
[-1.88237585e-03  2.53521799e+00 -5.68054428e+02]
[ 8.28355828e-04 -4.85513918e-01  8.69875145e+02]
52
[ 5.43794356e-04 -1.86959833e-01  1.86930347e+02]
[ 1.02002290e-03 -7.12327954e-01  9.35949088e+02]
53
[ 5.17180172e-04 -1.18503147e-01  1.57600737e+02]
[ 1.19162924e-03 -9.12634874e-01  9.93467009e+02]
54
[3.17301171e-04 7.85554092e-02 1.10824518e+02]
[ 1.21336861e-03 -9.29576751e-01  9.94783844e+02]
55
[3.41935411e-04 4.60933781e-02 1.21308926e+02]
[ 1.19236877e-03 -8.97094687e-01  9.82778320e+02]
56
[3.17991574e-04 4.60970866e-02 1.30918271e+02]
[ 1.14312751e-03 -8.48963722e-01  9.71746019e+02]
57
[2.91372507e-04 4.44642102e-02 1.42543379e+02]
[ 5.38885754e-04 -1.62538620e-01  7.80900731e+02]
58
[2.66541300e-04 5.14839136e-02 1.48049999e+02]
[ 5.44998139e-04 -4.86003183e-01  1.15721385e+03]
59
[3.17030402e-04 1.17575063e-02 1.52729058e+02]
[ 5.96647737e-04 -5.77790245e-01  1.18450563e+03]
60
[ 3.23299178e-04 -5.27144723e-03  1.61143061e+02]
[ 3.04516975e-04 -2.92776874e-01  1.11891324e+03]
61
[2.39493419e-04 1.16841126e-02 1.83492196e+02]
[ 2.99056607e-04 -2.43852026e-01  1.11378777e+03]
62
[2.10225115e-04 2.03527898e-02 1.91788765e+02]
[ 1.86047994e-04 -1.96041935e-01  1.11830222e+03]
63
[ 2.33884961e-04 -6.74175700e-03  2.01183792e+02]
[ 1.90310023e-04 -2.24646509e-01  1.14246935e+03]
64
[-2.50185539e-05  1.41124631e-01  2.13981469e+02]
[ 2.31482987e-04 -2.69537752e-01  1.16128464e+03]
65
[-6.04708876e-05  1.47359632e-01  2.27346255e+02]
[ 1.87654260e-04 -2.83934303e-01  1.18067833e+03]
66
[-3.30533310e-05  1.12239848e-01  2.40554908e+02]
[ 2.81404803e-04 -3.87443677e-01  1.21160395e+03]
67
[8.34213979e-05 1.42473920e-02 2.57062425e+02]
[ 1.14207190e-04 -2.11005204e-01  1.17042530e+03]
68
[ 2.11111819e-04 -7.80304156e-02  2.65182175e+02]
[-2.70749868e-04  2.09914845e-01  1.05978837e+03]
69
[ 2.31415797e-04 -1.26491621e-01  2.87798545e+02]
[ 3.74776811e-04 -5.46173150e-01  1.28613221e+03]
70
[ 1.32257629e-04 -9.87691945e-02  3.10535223e+02]
[ 5.35922511e-05 -2.39329907e-01  1.24982907e+03]
71
[ 5.14657639e-04 -3.54374972e-01  3.20017204e+02]
[-2.14318889e-05 -4.18526323e-02  1.13278341e+03]
72
[ 6.82260166e-04 -4.64640439e-01  3.21957265e+02]
[ 7.68844762e-04 -4.55386271e-01  8.89007196e+02]
73
[ 6.40387580e-04 -4.15206341e-01  3.09792286e+02]
[ 8.41659189e-04 -5.54746020e-01  9.25451582e+02]
74
[ 1.34897468e-03 -1.09818609e+00  4.55853662e+02]
[ 6.19259702e-04 -2.62763005e-01  8.34094284e+02]
75
[ 1.31962948e-03 -1.05576973e+00  4.40867653e+02]
[ 6.33837152e-04 -2.76933710e-01  8.40519501e+02]
76
[ 1.25965241e-03 -9.96218446e-01  4.25813525e+02]
[ 2.37835249e-04 -1.44652425e-01  1.12226665e+03]
77
[ 1.31887650e-03 -1.04727133e+00  4.34450460e+02]
[ 6.33582176e-04 -2.28771176e-01  8.14802987e+02]
78
[ 4.33206555e-04 -2.02642685e-01  2.53940655e+02]
[ 5.86134440e-04 -2.88846893e-01  8.71855415e+02]
79
[ 6.81205403e-04 -4.37658218e-01  3.08182400e+02]
[ 6.03771499e-04 -2.80063272e-01  8.60010852e+02]
80
[-2.18918823e-04  1.42834664e-01  1.84061441e+01]
[3.40404234e-04 2.73304065e-02 7.72766571e+02]
81
[-3.43126853e-04  2.30937420e-01  6.89541758e+00]
[ 3.59146427e-04 -2.07025561e-02  7.96816271e+02]
82
[-3.20927043e-04  2.23576466e-01  1.19538513e+01]
[ 4.08885490e-04 -9.60984570e-02  8.27166884e+02]
83
[ 8.21708916e-04 -6.49475230e-01  3.25052192e+02]
[ 4.63808019e-04 -1.76074209e-01  8.56735156e+02]
84
[ 9.23917631e-04 -7.14837923e-01  3.31496943e+02]
[ 4.11174058e-04 -1.74492869e-01  8.78050116e+02]
85
[ 1.01204149e-03 -7.99486532e-01  3.50578811e+02]
[ 3.42615718e-04 -1.61049609e-01  8.98548396e+02]
86
[ 6.62062304e-05 -1.12465683e-03  2.04057242e+02]
[ 3.81201449e-04 -2.32200365e-01  9.30653142e+02]
87
[ 5.50853052e-04 -3.16919676e-01  1.38722716e+02]
[2.52117259e-04 7.72950957e-02 7.81032583e+02]
88
[-1.27450986e-04  1.88132350e-01  2.55746096e+02]
[ 6.90490554e-04 -3.26578041e-01  8.54428579e+02]
89
[-2.99493235e-04  2.89583701e-01  2.64264395e+02]
[ 3.61630564e-04 -7.50735469e-02  8.26539189e+02]
90
[-2.18755276e-04  2.40749626e-01  2.58484038e+02]
[-2.29699714e-05  4.65663078e-02  9.11629616e+02]
91
[1.50402001e-05 3.74454565e-02 2.87623125e+02]
[ 2.51306650e-04 -7.98364603e-02  8.72976705e+02]
92
[2.73208317e-05 2.10992370e-02 2.84863502e+02]
[ 2.58615995e-04 -1.01789717e-01  8.82171935e+02]
93
[ 3.25148219e-04 -1.97053787e-01  3.00311882e+02]
[ 2.79717897e-04 -1.45756460e-01  9.02040303e+02]
94
[ 6.30306357e-04 -4.70676537e-01  3.44262666e+02]
[ 1.39891914e-03 -1.11762888e+00  1.01005841e+03]
95
[ 5.84764822e-04 -4.74238021e-01  3.60385685e+02]
[ 1.44677298e-03 -1.23000040e+00  1.05754575e+03]
96
[ 9.56519841e-04 -8.16561306e-01  4.21921058e+02]
[ 1.75353424e-03 -1.56390100e+00  1.15745109e+03]
97
[-5.27066986e-04  9.36768351e-01 -1.58011467e+02]
[ 1.68461113e-03 -1.57322251e+00  1.19287552e+03]
98
[-1.91987472e-04  5.65689078e-01 -5.52422567e+01]
[ 1.77280322e-03 -1.68021321e+00  1.24620341e+03]
99
[2.19027736e-04 9.50468987e-02 8.25062398e+01]
[ 1.95667135e-03 -1.93768149e+00  1.33784623e+03]
100
[ 1.50171643e-03 -1.21403526e+00  4.20417652e+02]
[ 1.47914455e-03 -1.54532766e+00  1.29061981e+03]
101
[ 9.88351721e-04 -7.10823078e-01  3.09284337e+02]
[ 2.26754005e-03 -2.41564609e+00  1.52204024e+03]
102
[-6.50831069e-05  3.88766287e-01  3.41871764e+01]
[ 2.68072864e-03 -2.95006882e+00  1.69032365e+03]
103
[-4.07629550e-04  7.29227678e-01 -3.68035609e+01]
[ 2.79934526e-03 -3.19954742e+00  1.80156944e+03]
104
[-5.02009021e-04  7.91385745e-01 -2.79581438e+01]
[ 4.20361221e-04 -3.78014032e-01  1.16686070e+03]
105
[-4.70064076e-04  7.19453183e-01  1.31432529e+01]
[ 2.38192803e-04 -1.97387990e-01  1.13714725e+03]
106
[-3.52093062e-04  5.71629230e-01  6.46814415e+01]
[ 3.10503010e-04 -2.60257430e-01  1.15831335e+03]
107
[-3.30304368e-04  4.89211531e-01  1.10302942e+02]
[ 2.89676300e-04 -2.85447907e-01  1.18672978e+03]
108
[-1.98797784e-04  3.16395543e-01  1.67640271e+02]
[-3.00917360e-05  1.57979941e-01  7.59901379e+02]
109
[ 1.41013675e-04 -3.61679016e-02  2.50788390e+02]
[ 2.54578184e-04 -1.28971635e-01  1.22214463e+03]
110
[ 9.73723373e-04 -6.89271587e-01  7.45380236e+02]
[-5.16621791e-05  1.31773456e-01  7.98576216e+02]
111
[ 1.00826576e-03 -7.10345123e-01  7.69278275e+02]
[4.12536133e-05 2.31733625e-02 8.44373170e+02]
112
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361
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[ 9.38253193e-05 -5.42279051e-02  1.17080418e+03]
362
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363
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364
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[4.91380407e-05 2.14614291e-02 1.07392471e+03]
365
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[4.32608456e-05 2.73772480e-02 1.03102614e+03]
366
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[ 1.08631756e-04 -2.13753903e-02  1.02086865e+03]
367
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368
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[5.96056679e-06 2.72437149e-03 8.75019868e+02]
369
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[1.97014154e-05 1.38735569e-02 8.64909913e+02]
370
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[ 7.65052472e-05 -7.55246491e-03  8.28060362e+02]
371
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[-3.35317113e-05  1.44797544e-02  7.51092296e+02]
372
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[-2.66540646e-05  2.81331298e-02  7.26385990e+02]
373
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[ 2.71861896e-05 -3.63736665e-03  7.21624488e+02]
374
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[ 4.66361923e-05 -1.89094623e-02  7.01739494e+02]
375
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[ 5.77156805e-05 -1.68395908e-02  1.08934205e+03]
376
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[ 4.30317125e-05 -9.90289245e-03  1.07185535e+03]
377
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[ 9.01442487e-05 -1.40646506e-02  1.21912208e+03]
378
[-1.52289829e-05 -1.43538587e-02  6.18832266e+02]
[ 8.70544490e-05 -1.94246559e-02  1.21975111e+03]
379
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[ 9.79548768e-05 -2.39880697e-02  1.22007139e+03]
380
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[ 9.02770566e-05 -2.17705893e-02  1.21998432e+03]
381
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[ 8.18231463e-05 -1.78949190e-02  1.21969723e+03]
382
[-1.68190614e-05 -2.37447609e-02  5.92945336e+02]
[ 6.61371632e-05 -1.10238672e-02  1.21835381e+03]
383
[ 1.43918304e-05 -3.53480930e-02  6.04708211e+02]
[ 6.04984424e-05 -1.04659453e-02  1.21834457e+03]
384
[ 9.92053845e-05 -8.08252930e-02  5.80700771e+02]
[ 8.20128656e-05 -2.09094397e-02  1.21913359e+03]
385
[ 2.92220890e-05 -3.56414743e-02  5.86533824e+02]
[-4.61682814e-05  3.60336889e-02  9.83154212e+02]
386
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[-7.21325472e-05  4.91177147e-02  9.86117791e+02]
387
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[-3.31062898e-05  2.95535788e-02  9.72655075e+02]
388
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[-1.05247962e-05  1.76163591e-02  9.67642717e+02]
389
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[-2.36638220e-05  3.05726577e-02  9.56056306e+02]
390
[-8.97655084e-05  6.35016201e-02  6.14337657e+02]
[-4.44450671e-05  5.07332083e-02  9.49669069e+02]
391
[-6.45783041e-05  5.56724989e-02  6.21035678e+02]
[-3.68063227e-05  5.34929527e-02  9.30001864e+02]
392
[-1.42101934e-04  1.24693483e-01  6.03145856e+02]
[-8.42049477e-05  1.02408071e-01  9.08157776e+02]
393
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[-8.00025131e-06  3.48981385e-02  9.20323829e+02]
394
[-2.67281087e-04  2.43049130e-01  5.80337465e+02]
[2.79721911e-05 9.42886821e-04 9.23396202e+02]
395
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[-5.67699441e-05  9.34151973e-02  8.88274401e+02]
396
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[-5.85345592e-05  7.90782893e-02  1.07897733e+03]
397
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[-1.89554202e-05  3.93741519e-02  1.09224084e+03]
398
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[-2.15222796e-04  2.36512394e-01  1.04124346e+03]
399
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[-2.43203643e-04  2.62255746e-01  1.05415273e+03]
400
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[-3.49004646e-04  3.69087476e-01  1.03784501e+03]
401
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[ 4.23970982e-05 -8.74644287e-03  9.11859378e+02]
402
[ 1.04456653e-04 -1.05631369e-02  2.62385475e+02]
[-1.84269158e-04  1.96369600e-01  1.07971580e+03]
403
[1.28944797e-04 8.40837902e-03 2.28058838e+02]
[-2.05784585e-04  2.18646648e-01  1.09742029e+03]
404
[9.30524330e-05 5.04562516e-03 2.26023899e+02]
[-2.07883107e-04  2.19916643e-01  1.09854919e+03]
405
[6.87134715e-05 5.23915841e-03 2.23208730e+02]
[-2.21327982e-04  2.34173885e-01  1.10144245e+03]
406
[5.16241333e-05 4.73325066e-03 2.22642088e+02]
[-1.68799571e-04  1.64457537e-01  1.02509923e+03]
407
[ 9.82228202e-05 -1.19026771e-02  2.28206176e+02]
[-1.38778648e-04  1.38297404e-01  1.03547667e+03]
408
[8.08412279e-05 1.65719558e-02 2.14490471e+02]
[-1.41484990e-04  1.38628093e-01  1.04583487e+03]
409
[ 1.53601908e-04 -1.50999948e-02  2.04922642e+02]
[-9.93899309e-05  1.01549204e-01  1.05525475e+03]
410
[1.68405579e-04 7.72549978e-03 1.64107917e+02]
[-6.02946735e-05  6.81654098e-02  1.06638677e+03]
411
[2.07449932e-04 1.33150413e-02 1.44692642e+02]
[-3.18983842e-05  4.31651892e-02  1.08421192e+03]
412
[1.07155217e-04 7.07414398e-02 1.48501491e+02]
[1.14719780e-05 1.06996311e-03 1.06736574e+03]
413
[-6.41983949e-05  5.70274669e-02  4.09042530e+01]
[-1.18179137e-04  1.18155104e-01  1.15995939e+03]
414
[ 2.43000781e-04 -9.71917432e-02  4.72642090e+01]
[-9.72587020e-05  1.01788902e-01  1.14431358e+03]
415
[-5.20556099e-04  8.10614797e-01 -1.95378321e+02]
[-9.31987828e-05  1.00339955e-01  1.16702407e+03]
416
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[-3.89315867e-05  5.15112157e-02  1.15774354e+03]
417
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[-2.95116981e-05  4.10117104e-02  1.15555549e+03]
418
[-6.40146498e-04  4.19510927e-01  2.70016262e+02]
[-1.31936353e-04  1.34370867e-01  1.11861293e+03]
419
[-7.06597082e-04  5.13112599e-01  2.47216493e+02]
[-1.19060170e-04  1.25756904e-01  1.10444333e+03]
420
[ 3.73101576e-04 -7.53998373e-02  5.43276724e+01]
[-6.35048823e-05  7.03611658e-02  1.09440521e+03]
421
[ 4.41248997e-04 -2.05212621e-01  1.09048912e+02]
[-6.10172965e-05  6.56499731e-02  1.08955211e+03]
422
[ 2.80844304e-04 -9.30839545e-02  1.00669795e+02]
[-5.13939593e-05  5.40064900e-02  1.09998945e+03]
423
[-6.56876608e-05  5.54680813e-02  6.97025729e+01]
[-1.17165530e-05  1.66619780e-02  1.11588981e+03]
424
[-7.63992871e-05  1.07528350e-01  6.58176508e+01]
[ 2.46784163e-05 -2.56906543e-02  1.13356262e+03]
425
[-9.24125919e-05  1.14556549e-01  5.97479334e+01]
[ 3.59377863e-05 -4.03916601e-02  1.15224963e+03]
426
[ 1.51740751e-04 -6.48401271e-02  9.65933170e+01]
[ 5.95865960e-05 -7.22336198e-02  1.17667768e+03]
427
[-8.19657867e-06  5.28891719e-02  6.98475121e+01]
[ 7.46735470e-05 -9.49201017e-02  1.19485942e+03]
428
[-1.39214839e-04  1.26575823e-01  4.98706201e+01]
[-1.87095369e-04  1.70843788e-01  1.16014691e+03]
429
[-1.74161813e-04  1.84877780e-01  3.67768671e+01]
[-1.03372855e-04  9.37160206e-02  1.18930506e+03]
430
[-2.56340246e-04  1.78176017e-01  2.48277630e+01]
[-2.15664056e-06 -2.51686935e-02  1.21370736e+03]
431
[-2.70551684e-04  1.99725098e-01  1.91580487e+01]
[-2.34154986e-04  2.27190623e-01  1.14782776e+03]
432
[-1.80625433e-04  9.06449436e-02  4.93124379e+01]
[ 2.04021716e-04 -1.02818421e-01  1.02554075e+03]
433
[-1.56466319e-04  1.08421216e-01  3.26579981e+01]
[ 1.99480325e-04 -9.82578052e-02  1.03225335e+03]
434
[-1.66289037e-05  1.95913082e-02  3.85686221e+01]
[ 2.17270169e-04 -1.42134798e-01  1.05733909e+03]
435
[ 1.23969245e-03 -1.07578218e+00  4.18763475e+02]
[ 1.63160944e-04 -1.24948280e-01  1.06950723e+03]
436
[ 1.03004714e-03 -8.77951972e-01  3.79736222e+02]
[ 2.49861037e-04 -2.21082885e-01  1.09710660e+03]
437
[ 7.57974326e-04 -6.08399318e-01  3.21212283e+02]
[ 1.94149245e-04 -1.65679206e-01  1.08245155e+03]
438
[ 6.76343083e-04 -5.33864655e-01  3.08063255e+02]
[-2.20778614e-04  2.53856398e-01  9.85443456e+02]
439
[ 8.46713968e-04 -7.00200238e-01  3.45234182e+02]
[-2.05906930e-04  1.81864574e-01  1.02530702e+03]
440
[ 8.69494055e-04 -7.26436746e-01  3.54028817e+02]
[-6.35922477e-05  3.18610076e-02  1.07224987e+03]
441
[ 1.86173699e-03 -1.78840515e+00  6.19552575e+02]
[-2.44059135e-04  2.13800374e-01  1.06240802e+03]
442
[ 1.67025883e-03 -1.58749415e+00  5.72379674e+02]
[-1.65316104e-04  1.47625877e-01  1.07175865e+03]
443
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[-5.29543190e-05  8.01200912e-02  1.13177231e+03]
444
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[-6.85139277e-05  2.13190354e-02  1.14392939e+03]
445
[-4.48617777e-04  3.37879438e-01 -1.13722025e+01]
[-1.25951432e-05 -1.87424581e-02  1.15692267e+03]
446
[-3.99843641e-04  3.27707360e-01 -9.69859787e+00]
[-3.95409350e-04  2.80840952e-01  1.14566957e+03]
447
[-9.57759166e-04  7.50689651e-01 -8.86265298e+01]
[-6.77391770e-05  2.88145848e-04  1.22654496e+03]
448
[-7.67659160e-04  5.92471122e-01 -5.87821771e+01]
[-3.72206710e-05  1.72273133e-02  1.23061339e+03]
449
[-5.91900340e-04  4.64342460e-01 -3.50262845e+01]
[-2.03264726e-04  1.29439248e-01  1.22526318e+03]
450
[-4.52158410e-04  3.48854266e-01 -1.38335538e+01]
[-1.46526333e-04  9.47467019e-02  1.23407746e+03]
451
[-4.03223283e-04  3.15156223e-01 -8.35145283e+00]
[-5.09052062e-05  5.32071236e-02  1.22866922e+03]
452
[-3.91655889e-04  3.06348161e-01 -6.21142380e+00]
[ 1.73700420e-05 -1.02868235e-02  1.24528535e+03]
453
[-9.96738685e-05  5.06053951e-02  4.32567562e+01]
[-2.12621301e-04  1.91279612e-01  1.10709925e+03]
454
[-6.35944289e-05  1.98406231e-02  4.81795811e+01]
[-1.32258925e-04  1.14492335e-01  1.13389894e+03]
455
[-6.62595953e-05  1.87093662e-02  4.95402911e+01]
[ 1.25726361e-03 -9.01370527e-01  9.29408727e+02]
456
[-1.97690814e-04  1.29530171e-01  2.89997480e+01]
[ 2.13873756e-03 -1.87335130e+00  1.18469229e+03]
457
[-1.84386796e-04  1.15241349e-01  3.38589618e+01]
[ 1.33505455e-03 -7.92216203e-01  8.20398452e+02]
458
[-1.11841428e-04  6.76997603e-02  3.90283118e+01]
[ 1.32775852e-03 -8.48778791e-01  8.60180285e+02]
459
[-9.14760253e-05  4.15049840e-02  4.74304128e+01]
[ 1.42252632e-03 -9.29120739e-01  8.70499591e+02]
460
[-1.06116510e-04  4.02051352e-02  5.06999313e+01]
[ 1.18235828e-03 -8.17399412e-01  9.00832973e+02]
461
[ 4.52430130e-06 -5.34194306e-02  7.02752006e+01]
[ 1.04337309e-03 -6.64248603e-01  8.58521614e+02]
462
[ 1.10059548e-04 -1.49914467e-01  9.28614830e+01]
[ 1.32495321e-03 -9.60198443e-01  9.28964378e+02]
463
[ 1.57751141e-04 -2.12798336e-01  1.13370037e+02]
[9.30137815e-05 8.33633569e-02 5.56315481e+02]
464
[-5.98966351e-04  4.40504601e-01 -1.66645814e+01]
[ 1.74365931e-03 -1.48039921e+00  1.08735831e+03]
465
[-5.86691531e-04  4.30893511e-01 -1.28820076e+01]
[1.58869820e-04 1.61801011e-03 5.86946328e+02]
466
[ 1.26284923e-03 -9.75225575e-01  2.83863598e+02]
[1.08324030e-04 4.51894699e-02 7.85489537e+02]
467
[-6.61508863e-04  4.70642623e-01 -1.33221659e+01]
[ 2.74667385e-04 -1.06131042e-01  8.17724381e+02]
468
[ 5.38565363e-04 -4.02878151e-01  2.84683500e+02]
[ 3.06123382e-04 -1.40784891e-01  8.20526302e+02]
469
[ 6.87307762e-04 -5.54210362e-01  3.18257406e+02]
[ 2.48562976e-04 -7.94094826e-02  8.04847722e+02]
470
[ 1.03371733e-03 -8.77202915e-01  3.75381076e+02]
[ 5.54984063e-04 -2.59752358e-01  7.92372334e+02]
471
[-9.74907357e-04  8.02837456e-01  4.36162174e+01]
[-7.83997957e-05  3.83268264e-02  6.42608176e+02]
472
[-6.31551676e-04  5.04070723e-01  9.62210313e+01]
[-2.01413283e-04  8.64657616e-02  6.53929108e+02]
473
[-1.16188471e-04 -1.92619839e-02  6.85326660e+02]
[-1.16188471e-04 -1.92619839e-02  6.85326660e+02]
474
[ 4.07270283e-04 -2.80213972e-01  2.39732850e+02]
[-7.51771068e-05  5.26085515e-03  6.67068329e+02]
475
[ 5.55204049e-04 -4.07712400e-01  2.58046760e+02]
[ 5.64142137e-05 -9.81241445e-02  6.85211076e+02]
476
[-3.84223243e-03  4.39112670e+00 -5.50381477e+02]
[ 1.46797746e-04 -1.67986019e-01  7.00710300e+02]
477
[-3.77362870e-03  4.29391232e+00 -5.18109188e+02]
[-3.76770483e-03  4.29773836e+00 -5.20700401e+02]
478
[-3.80040319e-03  4.28871764e+00 -5.08208849e+02]
[-3.71541721e-03  4.20495874e+00 -4.87271279e+02]
479
[-6.71806005e-04  4.94066163e-01  1.16884516e+02]
[-3.77490076e-03  4.32447074e+00 -5.23657008e+02]
480
[-3.69607239e-03  4.16702965e+00 -4.70369871e+02]
[-3.70900859e-03  4.18503234e+00 -4.70393706e+02]
481
[-3.35505521e-03  3.67357147e+00 -3.19732099e+02]
[-3.70699848e-03  4.12317372e+00 -4.41974521e+02]
482
[-3.36621844e-03  3.66752377e+00 -3.07446860e+02]
[-3.63812443e-03  4.03425426e+00 -4.09376326e+02]
483
[-3.33699266e-03  3.63677916e+00 -2.97599807e+02]
[-3.43762443e-03  3.76840918e+00 -3.33241637e+02]
484
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530
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532
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533
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535
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536
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537
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539
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540
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590
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592

/home/ubuntu_pgl/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:96: RankWarning: Polyfit may be poorly conditioned

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607
[ 6.93695429e-04 -8.06822486e-01  5.65896108e+02]
[ 5.42758949e-04 -5.78044903e-01  1.08857864e+03]
608
[ 2.03014764e-04 -2.80570401e-01  4.34659940e+02]
[ 2.83975249e-04 -3.73154052e-01  1.06017492e+03]
609
[ 3.33966813e-04 -4.07900806e-01  4.62392832e+02]
[ 9.59075593e-07 -1.26704188e-01  1.01610752e+03]
610
[ 1.71741429e-04 -2.66689286e-01  4.41599521e+02]
[-1.29680641e-04 -3.84972010e-02  9.99598093e+02]
611
[ 2.95854359e-04 -4.38130585e-01  5.01968046e+02]
[-2.07960398e-04  1.53133479e-02  9.89159058e+02]
612
[-1.68912885e-04  1.06662686e-01  3.47290885e+02]
[-2.41257547e-04  3.75440539e-02  9.85819091e+02]
613
[ 3.17756459e-04 -5.06570054e-01  5.36609104e+02]
[-2.47875496e-04  3.87300398e-02  9.90736989e+02]
614
[ 2.37479957e-04 -3.99362230e-01  4.98791620e+02]
[-2.71249445e-04  3.93373588e-02  1.00187357e+03]
615
[ 1.98633248e-04 -3.44028021e-01  4.76812680e+02]
[-2.93026679e-04  3.83197703e-02  1.01306227e+03]
616
[ 1.93106055e-04 -3.25196802e-01  4.65680095e+02]
[-2.94380373e-04  2.95124516e-02  1.02271643e+03]
617
[ 1.63726580e-04 -3.01078614e-01  4.62667501e+02]
[-3.16457404e-04  4.34795111e-02  1.02382837e+03]
618
[ 1.98088303e-04 -3.55807671e-01  4.84752277e+02]
[-3.23115800e-04  4.84759622e-02  1.02749937e+03]
619
[-8.09119458e-05 -2.20932449e-02  3.92307546e+02]
[-3.36444433e-04  6.88684656e-02  1.02122143e+03]
620
[ 1.25312292e-04 -2.66004149e-01  4.65272330e+02]
[-3.45933676e-04  7.14584433e-02  1.02504679e+03]
621
[-3.34335176e-04  1.77279923e-01  3.77372925e+02]
[-3.28432362e-04  8.37818699e-02  1.01392401e+03]
622
[-1.94088325e-04  9.63935590e-02  3.70659188e+02]
[-3.16112913e-04  8.62964142e-02  1.00847365e+03]
623
[-3.37715772e-04  1.89294285e-01  3.71674664e+02]
[-3.17815684e-04  9.66196969e-02  1.00258848e+03]
624
[ 4.84843480e-05 -7.67458003e-02  3.80611420e+02]
[3.62663642e-05 1.14073163e-02 1.13034599e+03]
625
[-9.48716490e-05  1.44801664e-02  3.87587220e+02]
[-3.04580397e-04  1.02464751e-01  9.97880960e+02]
626
[-1.30860931e-04  4.55031836e-02  3.83875589e+02]
[-3.61580695e-05  2.72915766e-02  1.13563859e+03]
627
[-1.37668883e-04  3.49914366e-02  3.91424911e+02]
[-1.05343907e-04  4.79133615e-02  1.14843739e+03]
628
[-1.70652233e-04  4.24837171e-02  4.00208287e+02]
[-2.41697575e-04  1.05056431e-01  1.15290812e+03]
629
[-1.70254164e-04  4.20131967e-02  4.00233722e+02]
[-2.56333375e-04  1.10304339e-01  1.16477972e+03]
630
[-1.29634973e-04  4.91441083e-02  3.78666692e+02]
[-2.33777873e-04  1.03326884e-01  1.17315912e+03]
631
[-2.75189492e-04  1.08233032e-01  4.08805267e+02]
[-8.58556475e-05  4.77485215e-02  1.15292736e+03]
632
[-1.61346056e-04  7.23242402e-02  3.79520492e+02]
[-5.05932299e-05  3.28812125e-02  1.14677857e+03]
633
[-1.76853962e-04  6.07849560e-02  3.88772412e+02]
[-6.70423644e-05  3.84563417e-02  1.15659058e+03]
634
[-3.21106436e-04  1.19661098e-01  4.07785700e+02]
[-1.50114419e-04  6.43981137e-02  1.17335311e+03]
635
[-2.22952241e-04  6.04731763e-02  4.02183938e+02]
[-2.87648058e-04  1.19105453e-01  1.21109462e+03]
636
[-1.79029858e-04  5.19416356e-02  3.89892258e+02]
[-1.46404089e-04  6.65935277e-02  1.17592370e+03]
637
[-2.37969379e-04  4.64856025e-02  4.16455137e+02]
[-1.92910094e-04  8.94341914e-02  1.17022506e+03]
638
[-2.53187651e-04  6.66417644e-02  4.11064763e+02]
[-2.35946446e-04  1.15834367e-01  1.16316431e+03]
639
[-1.49665368e-04  3.73134520e-02  3.95876837e+02]
[-6.50304659e-05  4.10920319e-02  1.11202511e+03]
640
[-1.28870931e-04 -1.30121170e-02  4.38626711e+02]
[-2.08146642e-04  1.18643236e-01  1.11634109e+03]
641
[ 1.26872734e-04 -1.57946317e-01  4.19233064e+02]
[-5.85321312e-05  3.74207438e-02  9.62359195e+02]
642
[ 1.38091721e-04 -1.64320086e-01  4.20089047e+02]
[-4.36671215e-05  3.18159087e-02  9.55707996e+02]
643
[-1.52574448e-04  1.59777213e-02  4.19016464e+02]
[-4.20955450e-05  3.08637254e-02  9.55787413e+02]
644
[-1.75252887e-04  3.60982256e-02  4.13404397e+02]
[-4.13095092e-05  3.06747852e-02  9.52773404e+02]
645
[-5.08168245e-05 -2.12786357e-02  3.97009316e+02]
[-4.20732847e-05  3.14034596e-02  9.53691616e+02]
646
[ 2.96940719e-05 -7.61756509e-02  3.96949857e+02]
[-3.83055192e-05  3.01219081e-02  9.53757342e+02]
647
[ 4.53399815e-05 -1.17861603e-01  4.24862851e+02]
[-2.69410701e-05  2.47012784e-02  9.47157693e+02]
648
[ 1.82606269e-04 -2.52172366e-01  4.53607536e+02]
[-1.87566453e-05  2.10813038e-02  9.41413525e+02]
649
[ 2.68510244e-04 -3.41388609e-01  4.74907384e+02]
[-1.69591618e-06  1.07930956e-02  9.37793186e+02]
650
[ 6.34639903e-04 -6.56252839e-01  5.30270554e+02]
[ 2.74688150e-05 -1.09769555e-02  9.37281602e+02]
651
[ 2.30601809e-04 -3.56182305e-01  5.11206396e+02]
[-2.28054174e-05  2.76752198e-02  9.26856708e+02]
652
[ 4.87503051e-04 -5.95905893e-01  5.48793287e+02]
[-2.31860880e-05  3.17064019e-02  9.21379815e+02]
653
[ 4.37650155e-04 -5.45371157e-01  5.34102924e+02]
[-5.72162028e-05  6.22120840e-02  9.22248860e+02]
654
[ 3.24267504e-04 -4.32379843e-01  5.07366977e+02]
[-6.40058517e-05  6.77586019e-02  9.21090328e+02]
655
[ 2.10834790e-04 -3.09085148e-01  4.77417059e+02]
[-5.70752462e-05  6.49061817e-02  9.04121403e+02]
656
[ 9.51437088e-05 -1.68925308e-01  4.35087424e+02]
[-3.46079266e-05  4.37200715e-02  9.03642299e+02]
657
[ 5.95184249e-05 -1.17100337e-01  4.15805739e+02]
[-2.16545805e-05  3.10007048e-02  8.97458707e+02]
658
[-4.05503959e-05 -1.50544824e-03  3.90390644e+02]
[-1.43476741e-05  2.52938197e-02  8.90402771e+02]
659
[ 9.33022273e-05 -1.13558482e-01  4.00899452e+02]
[-3.07291336e-05  4.07396545e-02  9.04166804e+02]
660
[ 1.74527397e-04 -1.77304706e-01  4.05389772e+02]
[ 2.87539926e-05 -1.19315757e-02  8.97552674e+02]
661
[ 2.21406934e-04 -2.16519657e-01  4.09542916e+02]
[-5.55838995e-05  7.03287773e-02  9.44134955e+02]
662
[ 2.68922212e-04 -2.56596674e-01  4.12686139e+02]
[-5.37141448e-05  6.90958255e-02  9.38200768e+02]
663
[ 5.57348248e-04 -5.34931384e-01  4.74913224e+02]
[ 2.41956595e-05 -8.19764334e-03  9.03275780e+02]
664
[ 7.57225641e-04 -7.41313948e-01  5.26322914e+02]
[-2.31649136e-05  3.35767053e-02  9.06614251e+02]
665
[ 8.07273897e-04 -8.05770056e-01  5.44747650e+02]
[-9.04002362e-05  1.02373822e-01  9.35414924e+02]
666
[ 7.98047183e-04 -8.10850962e-01  5.51701326e+02]
[-1.49443072e-04  1.56036416e-01  9.24016689e+02]
667
[ 7.75924098e-04 -8.03021667e-01  5.58015002e+02]
[-1.46796093e-04  1.57878348e-01  9.19674244e+02]
668
[ 7.03245866e-04 -7.49306246e-01  5.54811272e+02]
[-1.49588234e-04  1.59667946e-01  9.22146239e+02]
669
[ 6.78249913e-04 -7.41604050e-01  5.59148896e+02]
[-8.20576395e-05  9.73910847e-02  9.16708512e+02]
670
[ 8.28359603e-04 -9.29989742e-01  6.00819406e+02]
[ 9.02277747e-05 -7.34051316e-02  8.55286656e+02]
671
[ 5.83823528e-05 -5.76012347e-02  5.28949316e+02]
[-3.15603859e-04  3.36535148e-01  8.57709307e+02]
672
[ 1.20768143e-04 -1.34570802e-01  5.61192041e+02]
[-1.90464004e-04  2.16226699e-01  8.65393023e+02]
673
[ 1.55045689e-04 -1.77957029e-01  5.68877241e+02]
[ 1.43872625e-04 -1.44424348e-01  8.07387743e+02]
674
[ 2.63312187e-04 -2.90633398e-01  6.48162966e+02]
[ 2.99808911e-04 -3.26153018e-01  8.03974661e+02]
675
[ 2.46255877e-04 -2.68537623e-01  6.60132126e+02]
[ 1.41620953e-04 -1.59080210e-01  7.41102015e+02]
676
[ 5.49705101e-04 -6.22673159e-01  7.64364581e+02]
[-3.40974830e-04  3.92249949e-01  7.94954809e+02]
677
[ 5.37662580e-04 -6.16956563e-01  7.54750313e+02]
[-1.70923906e-04  2.10634264e-01  7.35992633e+02]
678
[ 2.13996559e-04 -2.55041774e-01  7.04647654e+02]
[ 4.36670857e-04 -4.77326283e-01  1.10578978e+03]
679
[ 4.08331861e-04 -5.11813579e-01  7.62768490e+02]
[ 6.37339306e-04 -7.32500096e-01  1.17549998e+03]
680
[ 7.12073750e-04 -8.79609186e-01  8.86225420e+02]
[3.25423998e-06 1.76700247e-03 7.33544989e+02]
681
[ 5.23228831e-04 -6.43779528e-01  8.34073584e+02]
[-3.21869468e-06  9.41688000e-03  7.16310506e+02]
682
[ 5.21668433e-04 -6.56421251e-01  8.41143311e+02]
[-3.11724533e-04  4.04001088e-01  6.11002067e+02]
683
[ 5.84156527e-04 -7.40445657e-01  8.71246248e+02]
[-5.27481997e-05  6.49298755e-02  6.86392171e+02]
684
[ 5.52429797e-04 -7.08585043e-01  8.64202131e+02]
[-2.38502940e-05  3.52609676e-02  6.86352391e+02]
685
[ 7.99774326e-04 -1.03458219e+00  9.69314090e+02]
[-2.39354882e-05  4.99514195e-02  6.83596830e+02]
686
[ 2.22338844e-04 -2.99974451e-01  7.36285024e+02]
[-1.13915874e-04  1.71369549e-01  6.53727793e+02]
687
[ 2.01084141e-04 -2.83466524e-01  7.23453965e+02]
[-4.29808754e-04  5.81295593e-01  5.17216272e+02]
688
[-1.01608377e-03  1.29690244e+00 -4.03069601e+01]
[-1.10631796e-04  1.63331313e-01  8.44216949e+02]
689
[-3.72980081e-04  4.44815775e-01  4.94373922e+02]
[-1.38691887e-03  1.78174569e+00  1.09008559e+02]
690
[-8.45331837e-04  1.05923131e+00  2.96487628e+02]
[-1.78982139e-03  2.30876499e+00 -6.16353875e+01]
691
[ 5.29780083e-04 -6.11294109e-01  5.36746294e+02]
[-1.34186282e-03  1.76604093e+00  1.00378206e+02]
692
[ 5.77239880e-04 -6.72129390e-01  5.55123543e+02]
[-8.93301870e-04  1.22641897e+00  2.59861680e+02]
693
[ 1.84426021e-04 -1.68071554e-01  3.91790501e+02]
[ 3.10220788e-04 -3.22316601e-01  7.16727824e+02]
694
[ 2.91238709e-04 -3.36762586e-01  2.78241935e+02]
[ 8.13246149e-04 -9.67591251e-01  9.17420437e+02]
695
[ 1.88063425e-04 -2.07826454e-01  2.38100516e+02]
[ 7.10765880e-04 -8.27202268e-01  8.70246166e+02]
696
[ 5.92761597e-05 -3.98103568e-02  1.84321847e+02]
[ 3.76474782e-04 -4.11272826e-01  7.43467010e+02]
697
[-9.61787027e-05  1.51700029e-01  1.28754395e+02]
[ 5.67172568e-04 -6.45657692e-01  8.12898223e+02]
698
[-1.00695048e-04  1.61557172e-01  1.26832536e+02]
[ 6.12196123e-04 -7.07888217e-01  8.34865237e+02]
699
[-2.58544599e-04  3.75068481e-01  5.06160874e+01]
[ 8.19573402e-04 -9.90041820e-01  9.30476395e+02]
700
[1.54519764e-05 3.20323901e-02 1.58645429e+02]
[ 6.31585849e-04 -7.58119956e-01  8.60493522e+02]
701
[ 3.47783222e-05 -2.08688911e-03  1.71410685e+02]
[ 5.29022994e-04 -5.99109197e-01  8.27542339e+02]
702
[ 5.22366251e-04 -6.30564326e-01  8.02474054e+02]
[ 5.76536104e-04 -6.46428493e-01  8.37524572e+02]
703
[ 2.09238318e-04 -2.85382364e-01  7.19542493e+02]
[ 1.63077429e-04 -1.61081632e-01  7.00670310e+02]
704
[ 1.68745503e-04 -1.84255567e-01  6.64417737e+02]
[ 1.14032507e-04 -1.51661903e-01  6.84451068e+02]
705
[ 5.05434384e-04 -6.55191186e-01  8.23004637e+02]
[-4.22641343e-06  2.43866378e-02  8.82173816e+02]
706
[ 4.53650316e-04 -3.52141585e-01  3.94229850e+02]
[-5.94638103e-04  4.18417310e-01  8.80689498e+02]
707
[ 1.76263405e-04 -1.88330800e-01  5.84350669e+02]
[-1.30489706e-04  8.73988871e-02  6.21915910e+02]
708
[-1.94987011e-04  7.20997468e-02  6.17783886e+02]
[ 4.57796644e-04 -5.14754813e-01  7.77292028e+02]
709
[-3.24410033e-04  2.36789063e-01  5.93606687e+02]
[-1.54763564e-04  1.46570691e-01  6.02640322e+02]
710
[-2.44518721e-04  1.61253213e-01  6.03366364e+02]
[-8.11629550e-05  8.24840856e-02  6.15123109e+02]
711
[ 3.33019477e-04 -4.13631269e-01  7.37245269e+02]
[ 8.06248403e-04 -5.50151048e-01  1.33181992e+03]
712
[ 1.06163081e-03 -1.05777978e+00  2.81168759e+02]
[ 3.13294725e-04 -2.18356743e-01  1.26851126e+03]
713
[ 6.32757851e-04 -6.81229320e-01  2.97778798e+02]
[ 1.30549295e-04 -3.88446609e-02  1.22738946e+03]
714
[ 1.26959500e-03 -1.50545116e+00  5.11785143e+02]
[-4.61261641e-05  1.20152584e-01  1.19172740e+03]
715
[ 4.80219174e-04 -5.19086373e-01  7.29017850e+02]
[-7.74119147e-05  1.73390457e-01  1.17525300e+03]
716
[ 5.52654569e-05 -1.28578329e-01  6.31395916e+02]
[-2.62297642e-04  3.51511642e-01  1.13494979e+03]
717
[ 1.19531214e-04 -1.49123658e-01  6.15173295e+02]
[-4.13409526e-04  4.64494216e-01  1.11377724e+03]
718
[-4.46553325e-04  1.65783141e-01  5.67783059e+02]
[-2.74639011e-04  3.31871972e-01  1.14430431e+03]
719
[-1.02953422e-03  5.61602733e-01  4.96413928e+02]
[4.21023201e-05 2.81917917e-02 1.21309929e+03]
720
[-1.25377796e-03  7.17501420e-01  4.59629009e+02]
[ 2.37352179e-04 -1.40832854e-01  1.24886178e+03]
721
[-4.20828010e-04 -5.59342152e-03  6.13390438e+02]
[ 3.32473209e-04 -2.24136653e-01  1.26237711e+03]
722
[-4.82825184e-04  5.57886189e-02  5.83852846e+02]
[ 2.35197746e-04 -1.25709816e-01  1.24249833e+03]
723
[-4.20812473e-04  5.59626175e-02  5.58742385e+02]
[ 3.08721435e-04 -2.01611817e-01  1.25530736e+03]
724
[-4.86602493e-04  1.26030062e-01  5.29866539e+02]
[ 1.65641782e-04 -1.06326457e-01  1.24132289e+03]
725
[-4.58598673e-04  1.50935509e-01  5.02252007e+02]
[ 2.05476120e-04 -1.37677191e-01  1.24442595e+03]
726
[-4.84180680e-04  2.01086210e-01  4.74733142e+02]
[ 1.67334883e-04 -8.11948461e-02  1.23116907e+03]
727
[-4.12611236e-04  1.98025250e-01  4.47281675e+02]
[ 1.52795708e-04 -6.75626810e-02  1.22432951e+03]
728
[-3.69419254e-04  2.06571156e-01  4.21002268e+02]
[5.19659588e-05 3.54856862e-02 1.20089550e+03]
729
[-3.78605102e-04  2.47679440e-01  3.97248885e+02]
[-9.66232386e-05  1.61876981e-01  1.17780006e+03]
730
[-3.39420418e-04  2.61406938e-01  3.69618895e+02]
[ 3.19411189e-04 -2.71549283e-01  1.17839761e+03]
731
[-3.05575305e-04  2.70712807e-01  3.47095749e+02]
[ 2.00349298e-04 -1.47876501e-01  1.13723696e+03]
732
[-3.43487548e-04  3.42514262e-01  3.13577233e+02]
[ 6.60678308e-05 -3.63273729e-03  1.08789111e+03]
733
[-3.64455701e-04  3.96908420e-01  2.82674672e+02]
[-1.26621131e-04  1.89827864e-01  1.03932467e+03]
734
[-3.16341425e-04  3.88812935e-01  2.64092830e+02]
[ 9.18348825e-04 -8.42270199e-01  1.41525100e+03]
735
[-4.16330973e-04  5.26311475e-01  2.14292444e+02]
[ 8.42253871e-04 -7.66262038e-01  1.39823382e+03]
736
[-4.38157581e-04  5.76422695e-01  1.88349420e+02]
[ 7.26692289e-04 -6.68408605e-01  1.37679095e+03]
737
[-4.29291897e-04  5.95759907e-01  1.68397480e+02]
[ 9.19867599e-04 -8.66021453e-01  1.42388587e+03]
738
[-3.81169612e-04  5.91131356e-01  1.50197367e+02]
[-3.63175413e-04  3.70976239e-01  8.77337622e+02]
739
[-7.02675304e-04  9.65025728e-01  4.19586390e+01]
[-3.84268790e-04  4.31406958e-01  8.43084908e+02]
740
[-1.00648285e-03  1.29046371e+00 -5.12470154e+01]
[-5.28208511e-04  6.13804391e-01  7.85478234e+02]
741
[-1.10782538e-03  1.48790634e+00 -1.35242819e+02]
[-5.70828056e-04  6.72742882e-01  7.58131740e+02]
742
[-1.36369939e-03  1.80931809e+00 -2.40844580e+02]
[-6.40779620e-04  7.60251944e-01  7.21863851e+02]
743
[-1.23258880e-03  1.69159414e+00 -2.27208860e+02]
[-5.80299339e-04  7.27254216e-01  7.14321218e+02]
744
[-1.14356459e-03  1.63891692e+00 -2.35358252e+02]
[-5.71370767e-04  7.56800421e-01  6.85997400e+02]
745
[-8.47880492e-04  1.33643308e+00 -1.65569481e+02]
[-7.34957768e-04  9.53958198e-01  6.16891549e+02]
746
[-1.48385917e-03  2.14292828e+00 -4.25039600e+02]
[-7.83657440e-04  1.02591611e+00  5.85854949e+02]
747
[-1.83580704e-03  2.60105742e+00 -5.76990505e+02]
[-8.72352106e-04  1.15584726e+00  5.32490203e+02]
748
[-1.75800366e-03  2.47775827e+00 -5.31541329e+02]
[-6.78944080e-04  1.00608645e+00  5.45485521e+02]
749
[3.08363499e-04 4.33908944e-03 1.87633564e+02]
[-4.32234765e-04  7.73726867e-01  5.89413218e+02]
750
[ 2.32828489e-03 -2.55216227e+00  9.84203680e+02]
[-6.39877122e-04  9.96995640e-01  5.27529720e+02]
751
[ 2.32925494e-03 -2.57286889e+00  9.94290813e+02]
[-9.52486206e-04  1.35364723e+00  4.26679187e+02]
752
[ 2.90591230e-03 -3.39023827e+00  1.27943393e+03]
[-6.05976670e-04  1.02976145e+00  4.93092810e+02]
753
[ 2.65936463e-04 -1.64986952e-01  2.93339210e+02]
[-2.15239341e-04  6.20255860e-01  5.93840366e+02]
754
[ 3.67006165e-04 -2.40609868e-01  3.02411199e+02]
[-6.77769000e-04  1.12181277e+00  4.54977917e+02]
755
[ 9.41211785e-05 -3.00922748e-02  2.84002723e+02]
[2.07519562e-04 1.26901909e-01 7.21051451e+02]
756
[ 5.34219326e-04 -2.91968675e-02  8.55989784e+01]
[3.33306045e-04 5.17836105e-02 7.16294981e+02]
757
[ 2.11790724e-05 -2.35693636e-03  2.98005322e+02]
[ 4.22180019e-04 -7.83961369e-02  7.60627541e+02]
758
[ 6.12291292e-05 -3.97981991e-02  3.05038294e+02]
[ 3.97033729e-04 -4.62098406e-02  7.47051048e+02]
759
[ 6.49037277e-04 -3.12750073e-01  2.28660280e+02]
[ 3.61113084e-04 -3.10845458e-02  7.48424477e+02]
760
[ 1.96675446e-04 -1.05543024e-01  3.04175962e+02]
[ 3.32565704e-04 -2.28877040e-02  7.54112867e+02]
761
[ 3.04603046e-04 -1.95950320e-01  3.20322481e+02]
[ 3.52622811e-04 -3.48850114e-02  7.50421599e+02]
762
[3.06507169e-04 1.31997216e-01 1.03355866e+02]
[3.22216039e-04 2.60030961e-02 7.17750287e+02]
763
[ 5.81480313e-04 -1.33852271e-01  1.58631380e+02]
[ 5.24508679e-04 -8.00565475e-02  7.13596858e+02]
764
[3.80546847e-04 4.83810713e-02 1.24662514e+02]
[ 5.20373098e-04 -6.86676336e-02  7.08438655e+02]
765
[3.02582834e-04 1.12797417e-01 1.16478015e+02]
[ 8.27049810e-04 -2.00731964e-01  6.78014344e+02]
766
[1.47373806e-04 2.57299795e-01 8.72475676e+01]
[ 3.87715517e-04 -1.04060330e-01  7.73317274e+02]
767
[-3.66074248e-06  3.73983281e-01  7.51913927e+01]
[ 4.09016844e-04 -1.42204750e-01  7.86313877e+02]
768
[2.23537817e-04 1.29501177e-01 1.37628587e+02]
[ 9.55484630e-04 -3.72200754e-01  7.29261675e+02]
769
[ 4.96898949e-04 -1.28243976e-01  1.89625118e+02]
[ 1.05212617e-03 -4.62569698e-01  7.54071976e+02]
770
[ 8.16463413e-04 -4.27582775e-01  2.48955928e+02]
[ 1.04167199e-03 -5.01995180e-01  7.69043789e+02]
771
[ 5.73818739e-04 -2.09319097e-01  2.10384060e+02]
[ 1.03478555e-03 -5.21129097e-01  7.83254088e+02]
772
[ 5.38374605e-04 -1.88899527e-01  2.12497288e+02]
[ 1.06952309e-03 -5.73290780e-01  8.02381994e+02]
773
[ 4.45616799e-04 -1.06297320e-01  1.98866691e+02]
[ 1.03531349e-03 -5.79831293e-01  8.17553035e+02]
774
[2.66865052e-04 6.57737333e-02 1.60720494e+02]
[ 1.03532738e-03 -6.14250763e-01  8.38230728e+02]
775
[-3.19622317e-05  3.83453532e-01  8.01322357e+01]
[ 1.01581326e-03 -6.32310819e-01  8.56290304e+02]
776
[1.36719787e-05 3.37461870e-01 8.98948175e+01]
[ 1.08563026e-03 -7.09686115e-01  8.81376505e+02]
777
[-2.46389656e-05  3.45702767e-01  1.01266434e+02]
[ 1.08270743e-03 -7.33657929e-01  8.97181869e+02]
778
[5.15099446e-05 2.34680397e-01 1.41943265e+02]
[ 1.00598838e-03 -7.06046202e-01  9.05556135e+02]
779
[-1.22198926e-05  2.91466953e-01  1.33486395e+02]
[ 9.74264616e-04 -7.12147199e-01  9.20233991e+02]
780
[-1.59770972e-05  2.65204068e-01  1.52648690e+02]
[ 9.07478385e-04 -7.01238073e-01  9.38708762e+02]
781
[ 2.37454290e-04 -2.34002364e-01  1.16745608e+02]
[ 6.98088300e-04 -5.64334463e-01  9.27634287e+02]
782
[ 3.27765300e-04 -3.05413578e-01  1.32546075e+02]
[ 5.20059915e-04 -4.04664958e-01  8.99690883e+02]
783
[ 3.13806022e-04 -2.94176646e-01  1.33252738e+02]
[ 2.68378284e-04 -1.59776259e-01  8.44284836e+02]
784
[ 3.69915071e-04 -3.28174116e-01  1.33853198e+02]
[ 3.04599432e-04 -1.82021852e-01  8.49258793e+02]
785
[ 3.42005250e-04 -3.09536975e-01  1.38786941e+02]
[ 2.71981038e-04 -1.82345946e-01  8.63644430e+02]
786
[ 3.82594058e-04 -3.51227831e-01  1.50366931e+02]
[ 2.55343679e-04 -1.88164261e-01  8.80003629e+02]
787
[1.78094179e-04 5.39023463e-02 2.14361633e+02]
[ 2.25016565e-04 -1.73747056e-01  8.83532344e+02]
788
[9.15942416e-05 1.10859473e-01 2.14874711e+02]
[-1.44921076e-05  5.16425872e-02  8.36704620e+02]
789
[2.08941614e-05 1.65329405e-01 2.11721299e+02]
[-5.17972244e-05  9.13041158e-02  8.27669021e+02]
790
[ 2.49699686e-04 -2.63239183e-01  1.72652485e+02]
[-7.44770500e-04  7.73924479e-01  9.20599462e+02]
791
[ 3.89364497e-04 -2.12646578e-01  3.04072053e+02]
[-4.74823233e-04  5.21563397e-01  9.67934878e+02]
792
[ 4.64457061e-04 -2.91463277e-01  3.26704270e+02]
[-2.77762013e-04  3.49062844e-01  1.01880054e+03]
793
[ 3.81576198e-04 -2.17715814e-01  3.17614105e+02]
[-5.59885576e-04  6.17754407e-01  9.63054666e+02]
794
[ 1.67188081e-04 -3.49503908e-02  2.91910594e+02]
[-4.84229010e-06  6.08092530e-02  8.54986023e+02]
795
[-1.42277922e-05  1.34020726e-01  2.62655808e+02]
[6.43147156e-05 7.68942804e-03 8.64809053e+02]
796
[ 1.10277314e-04 -2.65054429e-02  3.15769492e+02]
[ 1.41851090e-04 -6.27355796e-02  8.80993814e+02]
797
[ 5.50317454e-04 -4.92290574e-01  4.32084569e+02]
[ 2.61525550e-04 -1.71640632e-01  9.05481389e+02]
798
[ 5.97922049e-04 -5.50848074e-01  4.52133194e+02]
[ 3.04586939e-04 -2.01942213e-01  9.08306863e+02]
799
[ 5.31080031e-04 -5.01848017e-01  4.50043859e+02]
[ 5.82924214e-04 -5.16381956e-01  9.88606586e+02]
800
[ 1.84718981e-04 -1.39172350e-01  3.63574891e+02]
[ 5.75905508e-04 -5.54171588e-01  1.01512956e+03]
801
[-1.92177010e-05  6.48239166e-02  3.19747457e+02]
[ 6.66367181e-04 -6.80266308e-01  1.06306357e+03]
802
[-3.22841155e-04  3.82249220e-01  2.41186597e+02]
[ 7.42673467e-04 -8.03385293e-01  1.10549229e+03]
803
[ 3.87429621e-04 -4.46144272e-01  4.67550070e+02]
[ 9.02560689e-04 -1.00284666e+00  1.16467300e+03]
804
[ 2.06332147e-04 -2.33616409e-01  4.09691821e+02]
[ 1.02960096e-03 -1.17249647e+00  1.21765947e+03]
805
[ 8.71607245e-05 -9.19492837e-02  3.73340049e+02]
[ 1.08722976e-03 -1.27513908e+00  1.26090935e+03]
806
[-5.62964161e-04  6.68180335e-01  1.58980860e+02]
[ 1.09060685e-03 -1.31758207e+00  1.29190648e+03]
807
[-1.49981950e-03  1.83424207e+00 -1.98520310e+02]
[ 1.05968577e-03 -1.31154803e+00  1.30535978e+03]
808
[-8.15048695e-04  9.78042360e-01  4.96537975e+01]
[ 1.12375947e-03 -1.42260074e+00  1.34997874e+03]
809
[-1.18030204e-03  1.51401467e+00 -1.12589455e+02]
[ 1.20196607e-03 -1.54314709e+00  1.39507456e+03]
810
[-2.85810931e-04  3.77251818e-01  2.36747141e+02]
[ 1.31184689e-03 -1.70209412e+00  1.45264647e+03]
811
[-1.96644504e-04  2.65890465e-01  2.75502847e+02]
[ 1.30187558e-03 -1.72537108e+00  1.47413704e+03]
812
[-6.84108226e-04  9.31752392e-01  4.88962011e+01]
[ 1.41907620e-03 -1.91121301e+00  1.54343018e+03]
813
[ 1.42117913e-04 -2.38259537e-01  4.60440199e+02]
[ 1.21556636e-03 -1.73994418e+00  1.51860737e+03]
814
[-2.94315615e-03  3.56245781e+00 -6.88854778e+02]
[ 1.70272215e-03 -2.30824046e+00  1.68475951e+03]
815
[ 6.99114632e-03 -9.55821434e+00  3.63488827e+03]
[ 1.99913103e-03 -2.67326986e+00  1.79660011e+03]
816
[-6.16813141e-04  8.15638678e-01  5.94137627e+01]
[ 3.97417193e-04 -5.71336371e-01  1.10579238e+03]
817
[ 2.83209654e-04 -3.41559958e-01  4.88531826e+02]
[ 4.47984441e-04 -4.54961837e-01  8.08508616e+02]
818
[ 1.88681563e-04 -2.21825901e-01  4.46251333e+02]
[ 3.63244656e-04 -4.11182471e-01  7.51355089e+02]
819
[ 1.62555561e-04 -1.92351406e-01  4.35885997e+02]
[ 4.13353091e-04 -4.64180745e-01  7.62658423e+02]
820
[ 1.36630428e-03 -1.66127456e+00  8.78006162e+02]
[ 2.00160909e-04 -2.56121535e-01  1.23103388e+03]
821
[ 6.69956981e-05 -7.53362671e-02  3.76665417e+02]
[ 4.66951125e-04 -5.60264094e-01  8.04975626e+02]
822
[ 1.04661402e-03 -1.30927250e+00  6.50434427e+02]
[ 9.21215327e-05 -1.22529152e-01  1.11294549e+03]
823
[-3.32770910e-04  3.85687575e-01  1.96505002e+02]
[ 5.76833538e-05 -9.43090724e-02  1.10184673e+03]
824
[ 1.19261805e-03 -1.49694327e+00  1.09718931e+03]
[-5.08133023e-05 -4.28629958e-02  1.12087499e+03]
825
[ 8.17553250e-04 -1.00557615e+00  6.77849768e+02]
[-3.42657471e-04  5.75764855e-02  1.15222361e+03]
826
[ 2.46718890e-03 -3.10430915e+00  1.05649440e+03]
[-2.26600653e-04  8.05961554e-02  1.11135465e+03]
827
[ 2.67670814e-04 -4.35822346e-01  7.91982299e+02]
[-3.29011222e-04  1.84163162e-02  1.17168664e+03]
828
[-6.57134413e-04  5.28093310e-01  3.43694475e+02]
[-3.04911732e-04 -3.84116170e-03  1.16010955e+03]
829
[-4.57834699e-04  3.93658245e-01  3.39656602e+02]
[ 1.52753674e-04 -1.02277727e-01  1.23705095e+03]
830
[ 2.01622877e-03 -3.14793767e+00  1.61635197e+03]
[ 1.30029732e-04 -5.99696068e-02  1.23240550e+03]
831
[-8.36184203e-04  5.14961587e-01  4.42629457e+02]
[ 8.30051642e-05 -2.54132957e-02  1.22915962e+03]
832
[-7.34589327e-04  4.74016832e-01  4.26532285e+02]
[ 1.15974348e-04 -5.32674021e-02  1.23383177e+03]
833
[-9.49064723e-04  6.68572088e-01  3.92793332e+02]
[ 1.19563719e-04 -8.75855759e-02  1.24199947e+03]
834
[-3.10210191e-04 -1.83419856e-01  6.76792814e+02]
[ 1.78206561e-04 -1.20841879e-01  1.24285076e+03]
835
[ 4.28113702e-05 -6.03103836e-01  7.98442781e+02]
[ 1.16121377e-04 -7.58651169e-02  1.22440542e+03]
836
[ 2.47404988e-03 -3.55251433e+00  1.67677771e+03]
[ 1.53202826e-04 -8.48829090e-02  1.21926884e+03]
837
[ 1.93125832e-03 -2.83314291e+00  1.44497745e+03]
[ 1.58659692e-04 -1.14126807e-01  1.23348733e+03]
838
[-2.26946332e-04  1.00072431e-02  5.14266765e+02]
[ 2.17839409e-04 -1.39822050e-01  1.24327270e+03]
839
[-4.67940347e-04  2.70227053e-01  4.51243988e+02]
[ 1.47696894e-04 -7.36015942e-02  1.23163114e+03]
840
[ 6.81956924e-04 -7.65843705e-01  6.32706272e+02]
[ 9.86048269e-05 -2.65505096e-02  1.22153874e+03]
841
[-3.25920972e-04  1.81368495e-01  4.52658824e+02]
[ 5.88141018e-05 -6.18475784e-03  1.21891457e+03]
842
[ 1.42305181e-04 -1.79982927e-01  6.98878684e+02]
[5.78331437e-05 2.24789009e-03 1.21807843e+03]
843
[ 1.94149218e-04 -2.09098408e-01  6.86225484e+02]
[-3.76152591e-05  4.84379327e-02  1.21474112e+03]
844
[ 3.38269165e-04 -3.20497704e-01  6.84627653e+02]
[4.93772305e-05 7.85886004e-03 1.21758826e+03]
845
[-1.13397992e-04  6.83496560e-02  6.19027262e+02]
[2.32967588e-05 9.43074775e-03 1.21683436e+03]
846
[-4.87578129e-05 -2.62288230e-03  6.21175332e+02]
[-1.70452399e-05  2.20038804e-02  1.21519701e+03]
847
[ 1.40055548e-05 -3.52936936e-02  6.25331207e+02]
[-3.31960531e-05  2.93118569e-02  1.20670551e+03]
848
[ 1.42554357e-04 -1.62102294e-01  6.61412166e+02]
[-1.24367943e-05  1.96897734e-02  1.20140439e+03]
849
[ 8.46405895e-05 -9.57164866e-02  6.55843213e+02]
[-3.53514689e-05  2.99400064e-02  1.18068082e+03]
850
[ 2.29132547e-04 -2.48358474e-01  6.73265851e+02]
[-2.94005051e-05  2.70449542e-02  1.16389497e+03]
851
[-1.65052790e-04 -5.57808773e-02  6.77253005e+02]
[2.66308655e-06 1.20184116e-02 1.15600642e+03]
852
[-2.67327932e-04  3.42245337e-03  6.72561572e+02]
[8.62777858e-06 8.53664324e-03 1.15332376e+03]
853
[-2.00295511e-04  2.89219009e-02  6.83746858e+02]
[6.80328669e-06 9.05651366e-03 1.15330621e+03]
854
[-2.86069279e-05  1.77722035e-02  6.34936550e+02]
[4.57630392e-06 1.03144074e-02 1.13819987e+03]
855
[7.97472785e-05 9.98077106e-03 6.14783005e+02]
[-1.19538752e-06  1.27456108e-02  9.76041492e+02]
856
[5.75335913e-05 7.30774183e-03 6.17468025e+02]
[-7.38641593e-07  1.28404213e-02  9.81012337e+02]
857
[-4.22043615e-05  1.97365488e-02  6.41116795e+02]
[-2.60068606e-07  1.24699977e-02  9.74052381e+02]
858
[-7.02158561e-05  3.51117136e-02  5.77943943e+02]
[7.10424889e-07 1.18637181e-02 9.69110234e+02]
859
[-1.46715256e-04  4.78340297e-02  6.61145554e+02]
[2.20912174e-07 1.21021266e-02 9.66092441e+02]
860
[-1.93452247e-04  5.64860521e-02  6.80333829e+02]
[9.05226476e-07 1.17503972e-02 9.69120797e+02]
861
[-1.64255496e-04  5.26592539e-02  6.72192101e+02]
[3.21618008e-06 1.03967630e-02 9.52244184e+02]
862
[-1.63207287e-04  5.69430506e-02  6.67693155e+02]
[2.60643627e-06 1.08677532e-02 9.50192390e+02]
863
[-1.62673495e-04  6.28427847e-02  6.56945021e+02]
[2.44480458e-06 1.09596420e-02 9.52184245e+02]
864
[-1.29350042e-04  6.02552240e-02  6.52366564e+02]
[2.36401893e-06 1.10050026e-02 9.52180274e+02]
865
[-1.25413688e-04  6.32237790e-02  6.43837508e+02]
[2.03289184e-06 1.11940541e-02 9.51163444e+02]
866
[-1.47185519e-04  7.31186323e-02  6.52144038e+02]
[2.23745510e-06 1.10772293e-02 9.54173848e+02]
867
[-1.24104556e-04  6.51905211e-02  6.44510874e+02]
[2.53019430e-06 1.09121149e-02 9.52188368e+02]
868
[-9.96479065e-05  5.80679597e-02  6.43751493e+02]
[-3.02066067e-05  3.88078632e-02  9.87158640e+02]
869
[-8.58802492e-05  5.37685141e-02  6.40918630e+02]
[-4.46459691e-05  5.26360696e-02  9.39294713e+02]
870
[-1.04475329e-04  7.19565127e-02  6.33477728e+02]
[-4.99092305e-05  5.76151679e-02  9.26151420e+02]
871
[-1.08624432e-04  8.36750653e-02  6.34288096e+02]
[-1.55725598e-05  2.17017091e-02  7.63984633e+02]
872
[-1.09521394e-04  1.00118406e-01  6.14871893e+02]
[-9.73703209e-06  1.86983025e-02  7.54187803e+02]
873
[-7.34412569e-05  7.30538952e-02  6.20178588e+02]
[2.91289560e-06 7.92551549e-03 7.51244164e+02]
874
[-5.53584763e-05  5.75377331e-02  6.26014878e+02]
[-2.65323561e-05  3.53156511e-02  7.44877014e+02]
875
[-2.16114946e-05  2.80761031e-02  6.31601342e+02]
[-1.79261957e-05  2.83268314e-02  7.41071486e+02]
876
[-1.71441068e-05  2.55127037e-02  6.31929414e+02]
[-6.14714405e-06  1.83115817e-02  7.40802374e+02]
877
[-5.77091444e-06  1.61876206e-02  6.32555802e+02]
[-1.66828236e-05  2.82666601e-02  7.36698030e+02]
878
[-8.64977130e-06  1.77393306e-02  6.25380967e+02]
[-2.82617415e-05  3.36398715e-02  6.72530575e+02]
879
[-1.73660569e-05  2.71561796e-02  6.18346715e+02]
[-3.05027139e-05  3.76210641e-02  6.68522670e+02]
880
[-8.13194292e-06  1.88796719e-02  6.30932572e+02]
[-2.21325610e-05  3.25518406e-02  6.67104049e+02]
881
[ 4.80193908e-05 -3.36918445e-02  5.65708755e+02]
[-2.10282631e-05  3.10068252e-02  6.62442711e+02]
882
[ 2.42066038e-05 -1.11713337e-02  5.54406942e+02]
[-1.04709495e-05  2.24083289e-02  6.60841179e+02]
883
[ 4.31014115e-05 -2.96180191e-02  5.52610757e+02]
[-3.69569715e-05  4.99134552e-02  6.52202475e+02]
884
[ 1.00239471e-04 -9.21895218e-02  5.68863925e+02]
[-1.23971961e-05  2.47633467e-02  6.28164227e+02]
885
[ 1.71178138e-05 -6.49210551e-03  5.17949255e+02]
[-1.34379728e-05  2.59251860e-02  6.27851605e+02]
886
[ 6.63462764e-05 -5.98496438e-02  5.92050877e+02]
[-2.23459911e-05  3.56397825e-02  6.25310469e+02]
887
[-4.86528085e-05  6.27929743e-02  6.12542358e+02]
[7.40121149e-06 5.62483527e-03 9.28540973e+02]
888
[ 3.46924108e-04 -3.68359423e-01  4.21179220e+02]
[-1.53965189e-06  1.36643752e-03  9.67308927e+02]
889
[ 3.65257256e-04 -4.01280821e-01  3.89154859e+02]
[-1.62836115e-04  1.93683104e-01  5.82095804e+02]
890
[ 2.22081234e-04 -2.45752768e-01  4.15414875e+02]
[-1.84406034e-04  2.21697056e-01  5.73111372e+02]
891
[ 2.34231311e-04 -2.64771858e-01  4.22527661e+02]
[-2.99517921e-04  3.58418445e-01  5.33203528e+02]
892
[-3.23560296e-05  5.99776709e-02  3.61289169e+02]
[-3.99553352e-04  4.81925997e-01  4.95537173e+02]
893
[-4.59922952e-05  7.18751941e-02  3.62907146e+02]
[-4.85050934e-04  5.93046662e-01  4.61699096e+02]
894
[-1.20734741e-04  1.64849074e-01  3.32250148e+02]
[-5.83737060e-04  7.22922897e-01  4.16600713e+02]
895
[-1.38173154e-04  1.87488826e-01  3.20904960e+02]
[ 7.4577113e-04 -8.6738968e-01  1.2716936e+03]
896
[-1.26848796e-04  1.72126453e-01  3.20995979e+02]
[ 1.54932815e-03 -1.89163096e+00  1.53950715e+03]
897
[-1.65059605e-04  2.21374599e-01  3.03080012e+02]
[ 7.19759781e-04 -9.03925719e-01  1.24324139e+03]
898
[-2.53908492e-04  3.33474504e-01  2.66744138e+02]
[ 9.62493199e-04 -1.22024750e+00  1.34105352e+03]
899
[ 4.03700818e-04 -5.16057490e-01  5.10058419e+02]
[ 1.25816634e-03 -1.62815233e+00  1.48185138e+03]
900
[ 4.42053208e-04 -5.70843202e-01  5.27479230e+02]
[ 1.44819310e-03 -1.92957043e+00  1.59606213e+03]
901
[ 3.56856657e-04 -4.58270499e-01  4.90172580e+02]
[ 1.44128621e-03 -1.97636517e+00  1.63763590e+03]
902
[ 2.10781330e-04 -2.64347406e-01  4.24036571e+02]
[ 1.26625260e-03 -1.78768867e+00  1.59413996e+03]
903
[ 6.07267880e-04 -7.92761887e-01  5.88697950e+02]
[ 6.28384675e-04 -1.06562889e+00  1.40051512e+03]
904
[ 5.00553199e-04 -6.69910166e-01  5.51468206e+02]
[ 4.75830475e-04 -8.77201394e-01  1.34595681e+03]
905
[ 7.52585611e-04 -1.05944483e+00  6.95131631e+02]
[ 4.21734683e-04 -7.98118425e-01  1.31922834e+03]
906
[-1.52349077e-04  1.24086029e-01  3.12353156e+02]
[ 6.13594499e-04 -1.01720476e+00  1.38200359e+03]
907
[-1.29316148e-03  1.61295460e+00 -1.72123591e+02]
[ 8.54886437e-04 -1.29429180e+00  1.46078573e+03]
908
[-2.12469763e-03  2.72446372e+00 -5.45713031e+02]
[ 1.17981255e-03 -1.68024732e+00  1.57221830e+03]
909
[-2.20917828e-03  2.84191666e+00 -5.84085342e+02]
[ 4.37398889e-04 -7.58500285e-01  1.28681797e+03]
910
[-3.67479515e-03  4.76362398e+00 -1.20812265e+03]
[ 4.84331331e-04 -8.23014835e-01  1.30874164e+03]
911
[-2.99963536e-03  3.92426325e+00 -9.46632788e+02]
[ 5.30966371e-04 -8.42624206e-01  1.30297593e+03]
912
[ 5.91215373e-05 -6.16550105e-02  3.47082666e+02]
[ 5.42328176e-04 -8.32852038e-01  1.29134357e+03]
913
[-2.71955549e-05  6.08456125e-02  5.92365631e+02]
[ 8.10340049e-04 -1.13399449e+00  1.37105046e+03]
914
[-1.68363806e-04  2.36670993e-01  5.38628866e+02]
[ 6.66311677e-04 -9.66250178e-01  1.32027656e+03]
915
[-5.36867535e-05  8.44178369e-02  5.85266882e+02]
[-1.14953338e-03  1.55872941e+00  1.11892594e+02]
916
[ 4.25862116e-04 -5.42091590e-01  7.88808211e+02]
[-1.32937848e-03  1.79077536e+00  3.81211059e+01]
917
[ 1.56412390e-04 -1.99048383e-01  6.80237012e+02]
[-8.35447291e-04  1.13797048e+00  2.52926207e+02]
918
[ 3.27461042e-04 -4.21922405e-01  7.52308691e+02]
[-4.17900634e-04  6.46783195e-01  4.08316600e+02]
919
[ 4.11422997e-04 -5.31955824e-01  7.87709855e+02]
[-4.55574546e-04  6.94369249e-01  3.93585307e+02]
920
[-5.71536882e-04  7.50470043e-01  3.81696574e+02]
[-6.00443378e-04  7.82358705e-01  3.74049013e+02]
921
[-1.43559768e-03  1.75664000e+00  7.16679101e+01]
[ 5.42768370e-04 -7.93705878e-01  1.23997544e+03]
922
[-1.83053822e-03  2.08959415e+00 -2.16886579e+02]
[ 3.20527420e-04 -5.65332339e-01  1.19195015e+03]
923
[-4.20557390e-04  3.33896545e-01  3.25685815e+02]
[ 3.91917165e-04 -6.09906508e-01  1.19550117e+03]
924
[-1.34777986e-04  3.49582860e-02  3.99776750e+02]
[ 5.73257207e-04 -7.90697822e-01  1.23841978e+03]
925
[-5.11604981e-04  4.78996724e-01  2.71374311e+02]
[ 4.71980642e-04 -6.93387039e-01  1.21765832e+03]
926
[-5.17872110e-04  4.95659695e-01  2.61167697e+02]
[ 4.68513198e-04 -7.16701943e-01  1.23440719e+03]
927
[-6.07573085e-04  6.60782656e-01  1.98803833e+02]
[ 5.77005672e-04 -8.53892709e-01  1.27789071e+03]
928
[-1.41797465e-03  1.72225629e+00 -1.47557320e+02]
[ 5.48448500e-04 -8.28841889e-01  1.27274162e+03]
929
[-5.37143560e-04  5.74480591e-01  2.25856781e+02]
[ 5.92174150e-04 -8.86089848e-01  1.29077654e+03]
930
[-1.39398554e-03  1.67861857e+00 -1.26158965e+02]
[ 8.02228861e-04 -1.12483944e+00  1.35824675e+03]
931
[-4.78808582e-04  5.00471578e-01  2.49482964e+02]
[ 9.80378692e-04 -1.32500206e+00  1.41313993e+03]
932
[-1.79684696e-03  2.24644403e+00 -3.27055063e+02]
[ 1.17553802e-03 -1.54577056e+00  1.47392354e+03]
933
[-1.49852720e-04  9.74839588e-02  3.72882270e+02]
[ 1.14185128e-03 -1.53277442e+00  1.47765089e+03]
934
[-4.91085418e-05 -1.99171310e-02  4.06118328e+02]
[ 1.09881416e-03 -1.50010756e+00  1.47353487e+03]
935
[-2.82789808e-04  1.72758565e-01  3.79186088e+02]
[ 1.20433072e-03 -1.64899734e+00  1.52450262e+03]
936
[ 1.49802321e-04 -1.73108697e-01  4.18366126e+02]
[ 1.34082412e-03 -1.83282602e+00  1.58513518e+03]
937
[ 7.32533497e-06 -1.65156421e-02  3.76383073e+02]
[ 1.57044520e-03 -2.12633520e+00  1.67814926e+03]
938
[-2.45662353e-04  2.30035218e-01  3.23442311e+02]
[ 1.67614163e-03 -2.27944085e+00  1.73234939e+03]
939
[-6.18848004e-04  5.19249870e-01  2.97307417e+02]
[ 1.98679416e-03 -2.67404181e+00  1.85630819e+03]
940
[ 4.33041263e-04 -3.67196138e-01  4.24953015e+02]
[ 1.97583570e-03 -2.69809160e+00  1.87664502e+03]
941
[ 4.33600511e-04 -3.66087658e-01  4.25779655e+02]
[ 2.08121783e-03 -2.85280210e+00  1.93296252e+03]
942
[-6.30094169e-04  4.63473297e-01  3.48477754e+02]
[ 2.34573827e-03 -3.21009506e+00  2.05291458e+03]
943
[-6.70222861e-04  5.84596873e-01  2.86868892e+02]
[ 2.29645735e-03 -3.19743608e+00  2.06685184e+03]
944
[-7.05533296e-04  7.22995822e-01  2.11086399e+02]
[ 2.55242480e-03 -3.56168084e+00  2.19554612e+03]
945
[-7.01785055e-04  8.28342139e-01  1.34563527e+02]
[ 2.89505374e-03 -4.04064135e+00  2.36196769e+03]
946
[-7.52756052e-04  8.37140891e-01  9.96572344e+01]
[ 3.05125272e-03 -4.27282565e+00  2.44720923e+03]
947
[ 5.75030101e-04 -4.40438941e-01  3.70984073e+02]
[ 3.22348896e-03 -4.54654119e+00  2.55134253e+03]
948
[-6.56238587e-05 -8.13261722e-02  3.26003627e+02]
[ 3.38972312e-03 -4.82549876e+00  2.66137662e+03]
949
[ 1.66360607e-04 -5.11803418e-03  2.90096915e+02]
[-6.67629385e-04  3.35260504e-01  1.02680817e+03]
950
[-5.81149638e-04  7.76722242e-01  1.03611887e+02]
[ 4.20030110e-03 -6.00959306e+00  3.08916166e+03]
951
[-1.29649538e-03  1.67049739e+00 -1.73189181e+02]
[ 4.24753469e-03 -6.15482986e+00  3.16500480e+03]
952
[-2.68469128e-03  3.44339227e+00 -7.38360914e+02]
[-1.05887919e-03  7.39198014e-01  9.31935430e+02]
953
[-5.01322594e-03  6.51396220e+00 -1.75392880e+03]
[-1.48711586e-03  1.25592172e+00  7.79724639e+02]
954
[-7.87508983e-03  1.04527665e+01 -3.11099558e+03]
[-1.54176555e-03  1.28250096e+00  7.88714583e+02]
955
[-9.35581747e-03  1.23139914e+01 -3.68796253e+03]
[-1.85611857e-03  1.65430082e+00  6.83251866e+02]
956
[-1.87369212e-02  2.50951872e+01 -8.03694148e+03]
[-2.16322972e-03  2.02203220e+00  5.77208188e+02]
957
[-2.02805934e-02  2.66329414e+01 -8.52883594e+03]
[-2.78618681e-03  2.74997030e+00  3.71877434e+02]
958
[-3.04115738e-02  4.13729308e+01 -1.38553849e+04]
[-3.40221085e-03  3.47175182e+00  1.69786436e+02]
959
[-6.21412438e-04  5.06368902e-01  2.94159274e+02]
[-4.19403961e-03  4.39881736e+00 -8.99426264e+01]
960
[-6.73764389e-04  5.75637778e-01  2.67937638e+02]
[-4.56048994e-03  4.91591603e+00 -2.67701106e+02]
961
[ 1.13566764e-02 -1.59628282e+01  5.93862932e+03]
[-9.15620930e-04  9.02112708e-01  1.02036641e+03]
962
[ 7.00204951e-03 -1.02735635e+01  4.08596173e+03]
[ 2.08441744e-03 -2.22344013e+00  1.73837790e+03]
963
[ 2.18613229e-02 -3.09380420e+01  1.12613291e+04]
[ 6.11827441e-05 -1.28592167e-01  1.23292729e+03]
964
[ 9.12038859e-03 -1.01581211e+01  2.71524326e+03]
[ 7.97185644e-04 -9.49796482e-01  1.40331414e+03]
965
[ 1.30142644e-03 -2.85650521e+00  1.68547915e+03]
[-1.05145038e-03  1.02238450e+00  8.94222325e+02]
966
[ 2.65257506e-03 -4.83364294e+00  2.40806031e+03]
[-1.50166452e-03  1.52085417e+00  7.48777819e+02]
967
[-6.00327604e-03  7.13271832e+00 -1.72301794e+03]
[-2.68225312e-03  2.76937397e+00  4.11784837e+02]
968
[-4.57539327e-03  5.20498623e+00 -1.07120425e+03]
[ 2.08096511e-03 -2.18302847e+00  1.57833916e+03]
969
[ 1.31830113e-02 -1.87660568e+01  6.99997393e+03]
[-7.28341432e-04  4.73804049e-01  1.07880124e+03]
970
[-7.15607640e-03  8.61238447e+00 -2.18430014e+03]
[2.33979128e-05 7.42327157e-02 1.08140264e+03]
971
[-5.37976454e-03  6.20308777e+00 -1.36227781e+03]
[-3.61996131e-04  3.92303854e-01  1.00285514e+03]
972
[-1.35513108e-03  1.14428677e+00  2.31235906e+02]
[ 2.95212480e-05 -3.51513378e-02  1.02569402e+03]
973
[ 1.65862374e-02 -2.31509444e+01  8.44753224e+03]
[-1.35615616e-03  1.69082201e+00  5.68479771e+02]
974
[ 4.37243473e-03 -7.11498533e+00  3.17962174e+03]
[-1.95006007e-03  2.39658049e+00  3.38968442e+02]
975
[-1.09808385e-03  1.10489931e+00  3.79349762e+02]
[-2.54186014e-03  3.13854260e+00  9.04217846e+01]
976
[-5.26704487e-03  6.43788276e+00 -1.30348945e+03]
[-2.81031152e-03  3.50821358e+00 -5.31479727e+01]
977
[-5.32758590e-03  6.75696608e+00 -1.49143295e+03]
[-5.77050637e-05  7.68826868e-02  8.03263059e+02]
978
[-1.20072737e-02  1.47604056e+01 -3.90696980e+03]
[-6.62869589e-04  1.03490568e+00  6.16017801e+02]
979
[ 7.30643293e-03 -7.08510015e+00  2.23846306e+03]
[-3.70943696e-04  7.17167728e-01  6.81509387e+02]
980
[-1.56082000e-02  1.97588551e+01 -5.64907635e+03]
[-2.19938438e-02  2.40606593e+01 -5.76684002e+03]
981
[-1.60525607e-02  2.06142911e+01 -6.03198609e+03]
[ 6.16888536e-02 -6.72872217e+01  1.92438632e+04]
982
[-1.44993246e-02  1.88227470e+01 -5.54749747e+03]
[-2.81071460e-02  3.07050828e+01 -7.64446006e+03]
983
[-1.56184906e-02  2.06012780e+01 -6.24284457e+03]
[-1.64961160e-02  1.83097701e+01 -4.37439543e+03]
984
[-1.43046893e-02  1.92801166e+01 -5.95818358e+03]
[ 5.49516444e-02 -6.07843434e+01  1.76524843e+04]
985
[-2.57852958e-02  2.90174514e+01 -7.48303713e+03]
[ 4.22709216e-02 -4.71634491e+01  1.39799962e+04]
986
[-2.42392415e-02  2.76973580e+01 -7.25765313e+03]
[-4.27274032e-02  4.91000884e+01 -1.34123590e+04]
987
[-3.44181136e-02  3.99413957e+01 -1.09335925e+04]
[ 2.70613753e-02 -3.08940687e+01  9.60009040e+03]
988
[-3.27272317e-02  3.87087119e+01 -1.08098190e+04]
[-3.43060948e-02  4.08324282e+01 -1.14988044e+04]
989
[-2.31930863e-02  2.80508201e+01 -7.88761087e+03]
[ 3.28628595e-02 -3.88335906e+01  1.22022469e+04]
990
[-2.83270711e-02  3.49934203e+01 -1.02265810e+04]
[ 2.42337024e-02 -2.88739806e+01  9.30778775e+03]
991
[-2.86198094e-02  3.59896248e+01 -1.07603761e+04]
[ 1.09690536e-03 -8.64347180e-01  8.01170651e+02]
992
[-4.14773527e-02  5.28695148e+01 -1.62925759e+04]
[ 1.63341201e-02 -2.02433399e+01  6.92815158e+03]
993
[-4.92556182e-02  6.38029482e+01 -2.01219277e+04]
[ 2.24613662e-03 -2.68077759e+00  1.43142908e+03]
994
[-6.18575829e-02  8.15055014e+01 -2.63296491e+04]
[ 1.02393963e-03 -6.47947631e-01  6.13178077e+02]
995
[-5.89225423e-02  7.85632793e+01 -2.56866430e+04]
[ 2.22246776e-03 -2.20378335e+00  1.09998492e+03]
996
[-6.32035886e-02  8.55115035e+01 -2.84442506e+04]
[ 7.13957924e-02 -1.00922984e+02  3.67845371e+04]
997
[-7.22077483e-02  9.89452901e+01 -3.34382787e+04]
[-5.43739097e-03  5.04381247e+00  2.75058506e+02]
998
[-2.02386306e-02  2.74060587e+01 -8.74845454e+03]
[-3.26755900e-03  3.09878971e+00  5.51472560e+02]
999
[-6.11523817e-02  8.31142078e+01 -2.77362570e+04]
[-3.15822995e-03  2.92463185e+00  6.17674052e+02]
1000
[-1.38795428e-01  1.90988598e+02 -6.52154388e+04]
[-5.67112093e-03  5.35791639e+00  1.52965476e+02]
1001
[-2.22539709e-01  3.07874097e+02 -1.06011593e+05]
[ 4.90402057e-02 -7.14731834e+01  2.71321040e+04]
1002
[ 4.19914528e-03 -7.91639709e+00  3.63698152e+03]
[ 4.04307763e-02 -5.94455397e+01  2.29350705e+04]
1003
[-3.58740720e-02  4.94927464e+01 -1.69017129e+04]
[-2.52520472e-03  2.76102674e+00  5.25497534e+02]
1004
[-2.67174620e-01  3.79877169e+02 -1.34878878e+05]
[-7.79052193e-04  7.84748504e-01  1.06304946e+03]
1005
[-1.26593190e-03  5.68559200e+00 -3.29068922e+03]
[-8.42236314e-04  9.82289924e-01  9.53446397e+02]
1006
[-1.73547247e-04  4.24813253e+00 -2.81882087e+03]
[-1.31335181e-03  1.58163455e+00  7.34419052e+02]
1007
[ 1.77882169e-02 -2.10013017e+01  6.05590484e+03]
[-2.82579596e-03  3.36109772e+00  1.76428658e+02]
1008
[ 2.15350042e-02 -2.64884834e+01  8.06363603e+03]
[-1.85521950e-03  2.35695331e+00  3.90053445e+02]
1009
[ 2.29781039e-02 -2.77893922e+01  8.26406417e+03]
[-6.45479094e-04  9.38764820e-01  7.50935246e+02]
1010
[ 5.71245682e-03 -6.38755573e+00  1.94429626e+03]
[-9.70570890e-04  1.42419714e+00  5.33073880e+02]
1011
[ 1.53034760e-02 -1.39733190e+01  3.44670319e+03]
[ 4.84847806e-02 -5.38794064e+01  1.59383285e+04]
1012
[ 3.55266617e-04 -2.15071207e+00  1.10963866e+03]
[ 2.60047303e-02 -2.90312525e+01  9.02291546e+03]
1013
[ 5.63688182e-03 -6.83034421e+00  2.14801867e+03]
[ 1.97756566e-03 -1.41846291e+00  1.03276100e+03]
1014
[ 4.94018528e-03 -6.04135247e+00  1.92985529e+03]
[ 2.32425989e-03 -1.77517293e+00  1.06489575e+03]
1015
[ 9.14654293e-03 -1.11743271e+01  3.44431173e+03]
[ 4.17549312e-03 -3.29735763e+00  1.24243712e+03]
1016
[ 1.41050066e-02 -1.74273278e+01  5.36586915e+03]
[ 6.21822577e-02 -7.68935851e+01  2.45166734e+04]
1017
[ 1.34627962e-02 -1.67686460e+01  5.20544960e+03]
[ 1.19353522e-01 -1.51402470e+02  4.87187007e+04]
1018
[ 1.37732332e-02 -1.72113057e+01  5.35387319e+03]
[ 3.77810251e-03 -3.57050195e+00  1.57264564e+03]
1019
[ 2.19854136e-03 -2.49036477e+00  1.32166146e+03]
[ 2.22710186e-03 -2.51723492e+00  1.32798798e+03]
1020
[ 3.75251971e-03 -4.11362940e+00  1.66960970e+03]
[ 1.04339822e-04 -1.67516239e-01  1.26390065e+03]
1021
[ 3.64566203e-03 -3.99069557e+00  1.58057815e+03]
[ 2.93825011e-03 -2.44167510e+00  1.02258947e+03]
1022
[ 6.73707543e-03 -7.69914944e+00  2.59565000e+03]
[ 2.29768342e-03 -1.69677044e+00  9.68376714e+02]
1023
[ 9.45402662e-02 -9.73755125e+01  2.54546317e+04]
[ 7.55388728e-06 -1.08336063e-02  9.97987006e+02]
1024
[ 6.10067598e-03 -7.48236186e+00  2.57901957e+03]
[-1.10984220e-04  8.23692207e-02  9.06040125e+02]
1025
[ 1.27052377e-04 -1.34112652e-01  7.04571772e+02]
[ 7.39811804e-05 -6.38661245e-02  7.81099850e+02]
1026
[ 6.65166728e-05 -4.61847260e-02  6.28499617e+02]
[ 3.54381451e-04 -2.84004790e-01  7.82364945e+02]
1027
[ 4.37120425e-04 -3.87196291e-01  6.66525879e+02]
[ 1.71084213e-04 -1.12534187e-01  6.37008436e+02]
1028
[4.64147588e-05 1.32479073e-02 5.27893400e+02]
[-1.20869549e-04  7.82533357e-02  1.21459322e+03]
1029
[-1.54181892e-04 -1.07840089e-01  6.04756723e+02]
[-1.19347841e-05  1.85183490e-02  1.11060182e+03]
1030
[ 6.18331375e-05 -4.82259210e-02  6.10509876e+02]
[-2.73792628e-05  2.59764705e-02  1.05002014e+03]
1031
[ 1.24110908e-05 -1.39281658e-02  5.67616427e+02]
[ 1.25831260e-04 -6.41823372e-02  1.22910202e+03]
1032
[ 1.01402669e-04 -6.46871732e-02  5.76665616e+02]
[-4.80620481e-05  2.27959606e-02  1.18727119e+03]
1033
[ 1.54076346e-04 -1.35576780e-01  3.22850320e+02]
[ 4.08626603e-05 -4.02367881e-02  1.10846819e+03]
1034
[-1.92896704e-04  1.78128623e-01  2.06811730e+02]
[ 6.08098444e-05 -4.18172318e-02  1.06046811e+03]
1035
[-2.49886623e-04  2.46445235e-01  1.24840024e+02]
[ 9.29405708e-05 -7.30328573e-02  9.74765198e+02]
1036
[ 1.64927459e-04 -1.80124777e-01  2.02518456e+02]
[-4.67790061e-05  4.81210575e-02  9.02528959e+02]
1037
[ 3.30716848e-04 -3.37581847e-01  7.29883171e+02]
[ 3.78212945e-04 -3.78960451e-01  9.59181772e+02]
1038
[-3.86592930e-05  5.66455019e-02  5.97085469e+02]
[-1.04723109e-04  1.16704249e-01  7.91366969e+02]
1039
[ 1.63154430e-04 -2.15386888e-01  6.97912422e+02]
[ 1.92419259e-05 -1.20010444e-02  7.74071967e+02]
1040
[-2.26463073e-05  6.13172047e-03  5.89507982e+02]
[ 2.71104709e-04 -3.05720471e-01  1.26996295e+03]
1041
[-4.93781753e-04  5.47390643e-01  4.18162782e+02]
[ 1.77880715e-04 -1.83690186e-01  1.16916699e+03]
1042
[ 5.45303910e-05 -8.26066471e-02  5.55720689e+02]
[ 1.12789703e-03 -1.19696710e+00  1.54024252e+03]
1043
[-1.07101986e-04  1.56417913e-01  4.24432443e+02]
[ 4.33892404e-04 -4.88304905e-01  1.35126209e+03]
1044
[ 6.90924783e-04 -8.61675679e-01  8.75997163e+02]
[ 1.16822769e-04 -1.28967662e-01  1.20409913e+03]
1045
[ 1.10684227e-03 -1.41581518e+00  9.90908789e+02]
[-8.30217140e-05  1.09972771e-01  1.06497190e+03]
1046
[ 1.94649549e-03 -2.51288388e+00  1.29188278e+03]
[ 2.26231166e-04 -2.67748360e-01  1.25261221e+03]
1047
[ 2.25329072e-03 -2.93211000e+00  1.56061340e+03]
[-8.53736387e-05  1.11915897e-01  1.08452354e+03]
1048
[ 2.32201863e-03 -3.06199375e+00  1.57033629e+03]
[ 4.36491891e-04 -5.25918320e-01  1.29019966e+03]
1049
[ 3.00437041e-03 -4.00188658e+00  1.96187151e+03]
[ 1.56504699e-04 -1.79936389e-01  1.17172720e+03]
1050
[ 1.37849244e-03 -1.85186599e+00  1.25637027e+03]
[-6.78952956e-05  3.49855311e-02  1.21928141e+03]
1051
[ 1.93166250e-04 -4.08163946e-01  7.67937240e+02]
[ 4.44018889e-05 -7.94597411e-02  1.18105440e+03]
1052
[-7.54156682e-04  7.66722757e-01  3.60208701e+02]
[ 9.99482117e-04 -1.02059519e+00  1.48438192e+03]
1053
[ 2.11193544e-03 -2.78435751e+00  1.55033695e+03]
[ 1.45054427e-04 -1.83404562e-01  1.25456774e+03]
1054
[ 1.32572540e-03 -1.70926232e+00  1.18565259e+03]
[ 1.21850059e-04 -1.64812350e-01  1.14709067e+03]
1055
[ 1.33640411e-03 -1.70103048e+00  1.15053692e+03]
[-1.15485424e-06  7.84095893e-03  1.07749672e+03]
1056
[ 4.28361547e-04 -5.49149232e-01  8.13619527e+02]
[ 2.89262332e-04 -3.29561399e-01  1.22499979e+03]
1057
[ 1.29460829e-03 -1.62732928e+00  1.12686826e+03]
[-7.39303751e-05  8.45763203e-02  1.02450616e+03]
1058
[ 1.64609376e-03 -2.13161641e+00  1.25915258e+03]
[-3.31443121e-04  3.86608297e-01  8.43399376e+02]
1059
[ 1.55656197e-03 -2.09108292e+00  1.34095299e+03]
[-3.25086686e-04  3.97716686e-01  7.48100539e+02]
1060
[ 4.18920723e-04 -5.78897581e-01  8.38779141e+02]
[ 4.21632812e-05 -1.80601312e-02  7.90541618e+02]
1061
[ 1.39426097e-04 -2.02224147e-01  6.74249873e+02]
[ 7.04088645e-04 -7.96050003e-01  1.44991544e+03]
1062
[ 4.84883997e-04 -6.19838922e-01  8.27994644e+02]
[ 3.15885299e-04 -4.39527827e-01  1.28181971e+03]
1063
[-3.02388019e-04  4.30141605e-01  4.45024049e+02]
[ 3.40803160e-04 -4.51554995e-01  1.20148468e+03]
1064
[ 8.08014653e-04 -1.04380812e+00  8.63365579e+02]
[ 2.16661259e-04 -2.86780232e-01  1.17072904e+03]
1065
[-3.55691386e-04  4.31894870e-01  3.56762240e+02]
[ 3.02943227e-04 -4.09132240e-01  1.22895834e+03]
1066
[-2.15462950e-03  2.82772248e+00 -4.74395609e+02]
[ 2.08172721e-04 -2.78089984e-01  1.11290936e+03]
1067
[-3.35173554e-03  4.46347635e+00 -1.07173994e+03]
[-1.75665876e-04  1.77280214e-01  1.07415405e+03]
1068
[-5.67182269e-03  7.76501609e+00 -2.26095821e+03]
[ 4.89905741e-05 -9.01827548e-02  1.10075257e+03]
1069
[-4.38911782e-03  6.15722646e+00 -1.79913769e+03]
[ 2.42583695e-04 -4.03381747e-01  1.26999974e+03]
1070
[-4.63055487e-04  4.39765144e-01  5.64709726e+02]
[-2.30880949e-04  2.77259395e-01  1.15442599e+03]
1071
[-3.68595764e-04  3.84552586e-01  5.52634758e+02]
[-5.80087641e-04  6.08741333e-01  1.04886143e+03]
1072
[ 2.05253213e-05 -1.60512271e-01  7.33163297e+02]
[-5.53571968e-04  5.95381052e-01  9.79449214e+02]
1073
[ 6.88148409e-04 -1.14351826e+00  1.05068088e+03]
[ 3.33940171e-04 -4.05757182e-01  1.23444699e+03]
1074
[-3.47960650e-04  4.13560347e-01  5.15920748e+02]
[ 1.23471613e-04 -1.50837819e-01  1.09492645e+03]
1075
[ 3.53081551e-05 -2.71601626e-01  8.07528984e+02]
[-5.62419175e-05  1.40081313e-01  9.41697226e+02]
1076
[ 3.56424758e-04 -5.24540879e-01  8.29447159e+02]
[ 4.33779940e-05 -5.32563908e-02  9.73231818e+02]
1077
[-5.93101194e-04  6.65482532e-01  4.64038247e+02]
[ 1.26426539e-03 -1.43960473e+00  1.64092593e+03]
1078
[-7.16421519e-04  8.82191082e-01  3.26355080e+02]
[ 1.58143510e-03 -1.77273640e+00  1.72608032e+03]
1079
[ 2.91297620e-04 -3.08253322e-01  6.10589632e+02]
[ 3.04971484e-04 -3.83687002e-01  1.33913676e+03]
1080
[-1.04856443e-04  7.49338613e-02  4.81761921e+02]
[ 4.81946525e-04 -6.51470692e-01  1.34492828e+03]
1081
[-7.29130855e-04  7.92124641e-01  4.41402594e+02]
[ 1.56564663e-04 -2.43836651e-01  1.18441432e+03]
1082
[-1.19672407e-03  1.51510209e+00  1.62807051e+02]
[ 4.73105969e-04 -5.89219651e-01  1.22126803e+03]
1083
[-1.18986898e-03  1.51736852e+00  1.44841041e+02]
[-1.08702797e-04  6.65442739e-02  1.16312249e+03]
1084
[ 1.17968918e-04 -8.37013351e-02  6.01338435e+02]
[-3.20584889e-04  2.68643857e-01  1.06911602e+03]
1085
[-1.51968438e-04  2.17994208e-01  4.74955928e+02]
[ 8.71029382e-05 -1.08528423e-01  1.10168865e+03]
1086
[ 1.04427838e-04 -1.51085151e-01  6.69263003e+02]
[ 3.55142521e-05 -6.28936793e-02  1.03993480e+03]
1087
[-8.49405347e-05  9.00120787e-02  5.70677673e+02]
[ 2.27019105e-04 -2.24740804e-01  1.02336064e+03]
1088
[-5.68223889e-04  2.90572653e-01  6.82142306e+02]
[ 2.15558320e-04 -2.16045307e-01  9.80613019e+02]
1089
[-9.26232356e-04  7.03751171e-01  5.88581032e+02]
[ 9.62809138e-05 -7.56108113e-02  9.02884856e+02]
1090
[-4.80450801e-04  2.41960639e-01  6.76392891e+02]
[-2.21553712e-04  2.41171887e-01  7.94385963e+02]
1091
[ 7.45378524e-05 -2.26381689e-01  7.56467576e+02]
[ 2.07299163e-05 -2.52743561e-03  8.16634133e+02]
1092
[ 2.78091065e-04 -3.87630014e-01  7.41008610e+02]
[ 5.60913902e-05 -5.68645415e-02  8.09898960e+02]
1093
[ 1.87213895e-04 -2.14646777e-01  6.78504459e+02]
[ 2.51118360e-04 -2.53586784e-01  8.21582210e+02]
1094
[-1.31832251e-04  1.36164573e-01  6.15381847e+02]
[ 3.10489728e-04 -2.73415422e-01  7.88155268e+02]
1095
[ 2.87479192e-04 -3.11137453e-01  7.12964770e+02]
[ 2.54346329e-04 -1.59391610e-01  7.31217954e+02]
1096
[ 8.83405638e-05 -8.88839450e-02  6.68587728e+02]
[ 1.69388749e-04 -6.03897601e-02  6.71611962e+02]
1097
[2.06247780e-05 3.61827190e-02 6.04281288e+02]
[3.14592097e-05 6.02755061e-02 6.04257242e+02]
1098
[-5.65132416e-05  1.27257043e-01  5.40882540e+02]
[ 5.17608952e-04 -3.72204552e-01  7.16785408e+02]
1099
[-2.69240150e-04  2.86454474e-01  4.66598793e+02]
[ 6.32455476e-04 -5.23439505e-01  7.28383154e+02]
1100
[ 4.16998248e-04 -3.45544401e-01  6.67709147e+02]
[ 4.02915939e-04 -2.70396330e-01  6.61992256e+02]
1101
[ 2.36248078e-04 -1.92510720e-01  5.96267784e+02]
[ 2.53643935e-04 -1.07165877e-01  5.94814085e+02]
1102
[ 1.33070306e-04 -8.30361636e-02  5.39533417e+02]
[1.51556793e-04 6.59899862e-02 5.31792454e+02]
1103
[-7.00253412e-05  1.15058165e-01  4.66194482e+02]
[ 2.81859944e-04 -2.96516929e-01  7.19341129e+02]
1104
[-3.16569025e-04  3.54033189e-01  3.86949619e+02]
[1.37591490e-04 9.70919913e-02 5.03762710e+02]
1105
[-4.95103723e-04  5.57287379e-01  3.04776444e+02]
[ 3.68155062e-04 -3.81997474e-01  7.28622240e+02]
1106
[-4.90121102e-04  6.65211693e-01  2.41056706e+02]
[ 1.46097750e-04 -4.13781302e-02  6.21240703e+02]
1107
[-4.20853371e-04  5.00371790e-01  1.91812272e+02]
[-3.64370774e-04  3.35931878e-01  6.43945717e+02]
1108
[-3.59001678e-04  4.43442572e-01  1.56023487e+02]
[-3.13875894e-04  2.78736140e-01  6.25663793e+02]
1109
[-2.77771290e-04  4.29958744e-01  1.22795882e+02]
[-4.02418951e-04  4.02993931e-01  5.60934389e+02]
1110
[-2.53644336e-04  3.22326240e-01  9.00771561e+01]
[-3.15078338e-04  4.35309448e-01  4.98602160e+02]
1111
[-2.16786838e-04  2.60993380e-01  5.75572126e+01]
[ 2.17293922e-04 -2.82560139e-02  5.63642614e+02]
1112
[-1.61343472e-04  1.79280151e-01  4.28397610e+01]
[-5.84597479e-05  2.77036991e-01  4.86685743e+02]
1113
[-1.17690660e-04  1.55895562e-01  1.76201244e+01]
[ 5.78341423e-04 -4.98371924e-01  7.77879663e+02]
1114
[1.06301483e-05 3.45271264e-02 3.36805796e+01]
[ 2.79309210e-04 -1.66648425e-01  6.64159826e+02]
1115
[-6.34754997e-05  4.01370434e-02  4.85017051e+01]
[ 3.28108502e-04 -2.16968438e-01  6.47235794e+02]
1116
[-1.40628949e-04  7.80693437e-02  4.87567544e+01]
[ 4.37026879e-04 -3.72263307e-01  6.87515695e+02]
1117
[-6.72649504e-05 -3.51108236e-02  8.92078643e+01]
[ 9.58321590e-04 -9.07389517e-01  8.40597884e+02]
1118
[-4.12671763e-04  3.61333287e-01 -2.59892748e+01]
[-8.35533915e-06  2.62192963e-01  4.69187960e+02]
1119
[-5.40002456e-04  4.73252214e-01 -4.66853959e+01]
[-1.86172725e-05  1.88585306e-01  5.39454479e+02]
1120
[-7.06169672e-04  6.63719063e-01 -1.00397121e+02]
[-1.39845115e-04  3.94923535e-01  4.43768680e+02]
1121
[ 5.03675548e-04 -6.81389980e-01  2.68563926e+02]
[ 1.18987795e-03 -1.24114095e+00  9.66740068e+02]
1122
[ 5.08044972e-04 -7.35667550e-01  2.94753418e+02]
[-5.20513926e-05 -2.00588705e-02  6.92620066e+02]
1123
[ 2.59978627e-04 -4.29759801e-01  2.02596059e+02]
[-3.70343501e-04  2.17945640e-01  6.57671499e+02]
1124
[-1.17456371e-04  1.42144847e-03  7.85197588e+01]
[-1.27043662e-04 -2.06277890e-01  8.12472987e+02]
1125
[-7.02838391e-04  6.41830238e-01 -9.83864717e+01]
[-2.54559256e-04 -1.43818233e-01  7.98106109e+02]
1126
[-7.04331882e-04  6.89819951e-01 -1.32797563e+02]
[-3.97490469e-04  1.95520236e-02  7.39048651e+02]
1127
[-7.24372184e-05  3.67189491e-02  3.03949533e+01]
[-3.16795188e-04  4.56313113e-02  6.84719704e+02]
1128
[-8.04935975e-05 -1.88299388e-02  7.19259296e+01]
[-5.78380460e-04  4.07743349e-01  1.17966154e+03]
1129
[-4.10894124e-04  2.99364732e-01  9.79032033e+01]
[ 2.23572547e-04 -1.62842469e-01  1.12266950e+03]
1130
[-1.25219983e-04 -1.90116810e-02  1.49848490e+02]
[-2.36773628e-04  1.36967655e-01  1.23400924e+03]
1131
[-6.20171837e-04  3.98599807e-01  1.88785696e+01]
[-1.22873469e-04 -5.00650583e-02  1.15563583e+03]
1132
[-2.70473314e-03  2.50540531e+00 -4.77081934e+02]
[-2.68700071e-04  5.17084650e-02  1.15801237e+03]
1133
[-3.17745232e-04  2.40531630e-01  1.71344360e+00]
[ 3.80357391e-05 -2.21886199e-02  1.20229472e+03]
1134
[-3.53911616e-04  1.20182041e-01  7.39265222e+01]
[ 7.67590927e-05 -4.57377501e-02  1.19219760e+03]
1135
[-1.07808040e-04  1.45528681e-01  3.49722380e+02]
[ 2.61657450e-04 -2.16781879e-01  7.01521205e+02]
1136
[-7.07519862e-04  6.25389223e-01  2.80630980e+02]
[ 1.28228634e-04 -5.65097978e-02  1.23853944e+03]
1137
[-9.32177620e-04  8.68827965e-01  2.28440262e+02]
[ 7.00849932e-04 -5.60205720e-01  1.15876651e+03]
1138
[-1.77745629e-03  1.80838845e+00 -1.64249439e+01]
[ 7.44106064e-04 -4.21791643e-01  1.25291690e+03]
1139
[ 2.02880744e-05 -6.03980732e-02  4.04100344e+02]
[-3.76149284e-04  1.93813847e-01  1.22079793e+03]
1140
[-6.27965621e-04  6.77386643e-01  6.51434875e+01]
[-4.86043018e-05  1.18119064e-01  5.65538358e+02]
1141
[-8.34367047e-04  7.08891675e-01  6.60208712e+01]
[ 6.42792903e-05 -8.72945547e-02  6.59467016e+02]
1142
[-1.15526659e-03  1.01289192e+00 -3.52509219e+01]
[-4.08859360e-04  2.93644869e-01  1.20549144e+03]
1143
[-1.58827011e-03  1.45844371e+00 -1.58814038e+02]
[-8.03518179e-05 -5.66558443e-03  1.13924411e+03]
1144
[-1.15977774e-03  1.15223987e+00 -1.35456561e+02]
[-5.42377281e-04  3.89971472e-01  7.00002167e+02]
1145
[ 5.04883433e-04 -3.52695783e-01  1.72543334e+02]
[-7.93842527e-04  5.64703434e-01  6.38708602e+02]
1146
[ 5.10928387e-04 -3.72633206e-01  1.65082902e+02]
[ 7.21310966e-04 -3.53273047e-01  7.50670606e+02]
1147
[ 3.98309883e-04 -2.68022549e-01  1.22435099e+02]
[ 1.23164826e-03 -7.27023410e-01  7.99240075e+02]
1148
[ 3.14353292e-04 -1.98462519e-01  9.60915114e+01]
[ 8.68156603e-04 -5.65875716e-01  7.70554113e+02]
1149
[ 3.04052882e-04 -2.01901990e-01  8.66603602e+01]
[ 8.93830094e-04 -5.97706897e-01  7.62568279e+02]
1150
[-6.21505154e-05 -9.74135969e-02  1.12083337e+02]
[ 8.18539493e-04 -5.85345255e-01  7.51142051e+02]
1151
[-3.89334777e-04  1.04383999e-01  8.87680166e+01]
[ 9.57634532e-04 -6.86289959e-01  7.53961623e+02]
1152
[-9.22941611e-04  4.28359434e-01  5.44085872e+01]
[ 1.32410580e-03 -8.87599504e-01  7.57941591e+02]
1153
[ 1.47025273e-03 -9.89391855e-01  7.56132734e+02]
[ 9.49638298e-04 -6.26359822e-01  7.21304128e+02]
1154
[-1.27987588e-04  1.82622932e-01  2.94762045e+02]
[ 1.31185321e-03 -8.99772928e-01  7.58899984e+02]
1155
[-6.12194776e-04  4.91222227e-01  2.96737554e+02]
[ 1.35600681e-03 -9.36122251e-01  7.59765555e+02]
1156
[-4.24177948e-04  2.03091704e-01  1.90681251e+02]
[-3.47857320e-04  1.28538572e-01  1.15218363e+03]
1157
[-2.49422291e-04  1.14137418e-01  1.60413602e+02]
[-4.77761477e-04  1.73444666e-01  1.15191467e+03]
1158
[-5.13944733e-04  3.35458862e-01  1.56291073e+02]
[-5.24376405e-04  2.33731097e-01  1.12748247e+03]
1159
[-3.96887458e-04  2.66632758e-01  1.57466598e+02]
[-6.62860149e-04  3.31951849e-01  1.11038318e+03]
1160
[-1.16712333e-04  9.04522093e-02  1.73869398e+02]
[ 3.49425714e-04 -7.72401203e-01  1.40591162e+03]
1161
[-3.03227144e-04  2.24945453e-01  1.48903250e+02]
[ 3.29140020e-04 -7.37670965e-01  1.39029933e+03]
1162
[-1.61347953e-04  8.78269307e-02  9.58591948e+01]
[ 2.46150587e-05 -5.34917139e-01  1.35956208e+03]
1163
[-3.82477055e-04  3.00047794e-01  1.08035447e+02]
[ 1.70100319e-04 -6.35067821e-01  1.37792759e+03]
1164
[-3.40622365e-04  2.75928019e-01  9.52518549e+01]
[ 2.36874617e-04 -6.89010340e-01  1.38546533e+03]
1165
[-3.58614369e-04  2.86557598e-01  8.93964123e+01]
[ 2.24671326e-04 -6.65872105e-01  1.37536797e+03]
1166
[-5.92862853e-04  4.76291096e-01  4.71750229e+01]
[ 8.09137722e-05 -5.17820865e-01  1.33374943e+03]
1167
[-4.11967599e-04  3.58796034e-01  3.55940055e+01]
[ 1.23632945e-04 -5.61105524e-01  1.34478751e+03]
1168
[-4.67493687e-04  3.74351913e-01  5.39493367e+01]
[-1.38256890e-04 -2.76851343e-01  1.26619747e+03]
1169
[-6.73174169e-04  5.84414713e-01 -1.14485836e+01]
[-6.64681467e-05 -3.16953784e-01  1.26397549e+03]
1170
[-8.89183177e-04  7.51928416e-01 -2.91122325e+01]
[-5.33289067e-05 -3.57400223e-01  1.27907440e+03]
1171
[-9.08079138e-04  8.22386353e-01 -7.65808278e+01]
[-2.20790024e-04 -1.99152873e-01  1.24020987e+03]
1172
[-8.30687663e-04  7.57868371e-01 -7.04400760e+01]
[-9.40315585e-05 -2.68447054e-01  1.24455189e+03]
1173
[ 5.60487172e-04 -5.90607358e-01  2.42608779e+02]
[-2.52890144e-05 -3.11316004e-01  1.24739030e+03]
1174
[ 7.88522246e-04 -8.26942518e-01  3.08394415e+02]
[-2.62575347e-04 -1.12951114e-01  1.20069932e+03]
1175
[ 1.00641511e-03 -1.06249104e+00  3.66477743e+02]
[-8.52587633e-04  6.14561386e-01  5.28773792e+02]
1176
[ 1.03889626e-03 -1.08852044e+00  3.68305411e+02]
[-1.22950557e-03  8.96243466e-01  4.77881810e+02]
1177
[ 9.96562489e-04 -1.07251093e+00  3.65192328e+02]
[-2.19123700e-03  1.66916367e+00  3.46255356e+02]
1178
[ 5.77273043e-04 -6.42617837e-01  2.63062145e+02]
[-2.42119566e-03  1.91161212e+00  2.78846149e+02]
1179
[ 2.50317275e-04 -2.64939952e-01  4.32433191e+02]
[-2.25021502e-03  1.85557045e+00  2.59701129e+02]
1180
[ 2.75302237e-04 -2.87149196e-01  4.34862273e+02]
[-2.86278834e-03  2.40240606e+00  1.39626595e+02]
1181
[ 1.15786067e-04 -1.46571761e-01  4.12378328e+02]
[-3.30291649e-03  2.90872302e+00 -4.23963394e+00]
1182
[-1.35021703e-05 -2.95533290e-02  3.90296778e+02]
[-4.25764408e-03  3.86244834e+00 -2.38533410e+02]
1183
[-1.27238512e-05 -2.51433321e-02  3.85372758e+02]
[-3.97320206e-04  2.99296205e-01  9.68894248e+02]
1184
[ 1.52781436e-04 -1.83567461e-01  4.14623005e+02]
[ 1.47459308e-04 -2.73141188e-01  1.10581967e+03]
1185
[ 2.82816482e-04 -3.13117795e-01  4.38334829e+02]
[ 1.13707117e-04 -2.10304798e-01  1.07630822e+03]
1186
[ 2.65820222e-04 -2.82524567e-01  4.24220493e+02]
[ 1.37224364e-04 -2.09345444e-01  1.06247332e+03]
1187
[ 3.32021252e-04 -3.62528123e-01  4.44801962e+02]
[ 1.71368831e-04 -2.35230063e-01  1.06093842e+03]
1188
[ 2.10515824e-04 -2.26524800e-01  4.08812729e+02]
[ 2.18180629e-04 -2.71096981e-01  1.06142674e+03]
1189
[-3.21868745e-05  5.11825520e-02  3.38031040e+02]
[ 2.23843252e-04 -2.63341624e-01  1.05055487e+03]
1190
[-2.28234717e-04  2.62717119e-01  2.82263834e+02]
[ 2.15849288e-04 -2.51921522e-01  1.04253628e+03]
1191
[-3.76530216e-05  3.59019577e-02  3.48434751e+02]
[ 2.08337977e-04 -2.32931541e-01  1.03016983e+03]
1192
[-3.74928930e-04  4.34221101e-01  2.28347277e+02]
[ 1.84989705e-04 -2.05980987e-01  1.01927691e+03]
1193
[-3.33645450e-04  3.87681635e-01  2.34266432e+02]
[ 2.13675762e-04 -2.23749730e-01  1.01635051e+03]
1194
[-3.95038890e-04  4.67141528e-01  2.03564062e+02]
[ 2.12446849e-04 -2.18117630e-01  1.01017774e+03]
1195
[-2.75610020e-04  3.19844088e-01  2.43684611e+02]
[ 1.82857258e-04 -1.82255936e-01  9.95196744e+02]
1196
[-7.15554056e-04  8.45710160e-01  9.64024639e+01]
[ 1.93437893e-04 -1.93711058e-01  9.94778369e+02]
1197
[-6.12970214e-04  7.07976159e-01  1.28839486e+02]
[ 2.38846462e-04 -2.26418760e-01  9.93493385e+02]
1198
[-7.04754587e-04  8.07949203e-01  1.02850320e+02]
[ 3.00394851e-04 -2.82327116e-01  1.00118137e+03]

def process_image(image):
    # NOTE: The output you return should be a color image (3 channel) for processing video below
    # TODO: put your pipeline here,
    # you should return the final output (image where lines are drawn on lanes)
    color_image = np.copy(image)
    gray = grayscale(image)
    blur_gray = gaussian_blur(gray, 5)
    edges = canny(blur_gray, 40, 150)

    imshape = edges.shape
    vertices = np.array([[(0,imshape[0]),(imshape[1], imshape[0]), (imshape[1]/2, 310)]], dtype=np.int32)
    masked_image = region_of_interest(edges, vertices)
    
    left_line_xmin_last = None
    left_line_xmax_last = None
    right_line_xmin_last = None
    right_line_xmax_last = None
    line_y_max_last = None
    line_y_min_last = None
    
    line_edges = hough_lines(masked_image,rho=1,theta=np.pi/180,threshold=15,min_line_len=40,max_line_gap=20)
    line_image = weighted_img(color_image, line_edges, α=0.8, β=1., γ=0.)
    result = line_image
    return result

# Define a class to receive the characteristics of each line detection
class Line():
    def __init__(self):
        # was the line detected in the last iteration?
        self.detected = False  
        # x values of the last n fits of the line
        self.recent_xfitted = [] 
        #average x values of the fitted line over the last n iterations
        self.bestx = None     
        #polynomial coefficients averaged over the last n iterations
        self.best_fit = None  
        #polynomial coefficients for the most recent fit
        self.current_fit = [np.array([False])]  
        #radius of curvature of the line in some units
        self.radius_of_curvature = None 
        #distance in meters of vehicle center from the line
        self.line_base_pos = None 
        #difference in fit coefficients between last and new fits
        self.diffs = np.array([0,0,0], dtype='float') 
        #x values for detected line pixels
        self.allx = None  
        #y values for detected line pixels
        self.ally = None